People seem all too ready to anthropomorphize LLMs and AI chatbots like ChatGPT, Claude, and Gemini. Some humans even admit to ‘relationships’ with one or more of the various examples of machine intelligence. To illustrate how flawed this instinct could be, a Microsoft AI researcher built a tiny neural network inside Age of Empires II using goats, grass, and bridges. Adrian de Wynter shared his work in a paper dubbed If LLMs have human-like attributes, then so does Age of Empires II. The Microsoft researcher, based at the University of York, also talked to 404 Media recently about how he likes to turn absurdism up to 11 to make a point.

In the research paper, De Wynter doesn’t make the argument that LLMs do or do not actually have generalized anthropomorphic attributes. Instead, he illustrates that the AoEII goats can also power the kinds of models that lay behind today's most popular chatbots. That hammers home the argument that “in no case is a machine’s activity to be interpreted in terms of higher cognitive processes, if it can be fairly interpreted in terms of processes which stand lower in the scale of cognitive evolution and development.”

De Wynter also raises the well-known concept of confirmation bias. Those looking for human traits in tech like chatbots will tend to find them, he proposes. However, the big contrast between the absurdist goat example and the commercial LLM chatbot is the way people interact with them, the interface that makes the likes of Claude ‘conversation friendly.’ De Wynter’s research indicates that anthropomizing LLMs is a common trend in computer science papers. From 337 such papers De Wynter looked at, published in the last two years, he says that 57% assumed that LLMs could have human-like traits. This basic assumption could color the research, testing, and, of course, conclusions of these papers.

So, how did the Microsoft AI researcher build the goaty AoEII LLM? Well, he didn’t quite go as far as developing a full-blown LLM. Instead, De Wynter thought it sufficient to use AoEII’s scenario editor to build a working NAND gate, with a 1-bit perceptron, where the goats act as bits. This crude perceptron and the circuit to train it in-game are enough to demonstrate that the simplest building block of a modern neural network could be made this way. And if you think it is absurd that AoEII goats can embody consciousness, then it should be equally absurd to regard any of the well-known chatbots as anything more.

Companies behind the AI boom aren’t discouraging people from anthropomorphizing their wares. In many ways, they might benefit from these human perceptions. Chatbots they deploy are trained with natural language and use techniques to mimic the shape and tone of natural conversation. This makes it easy for users to project personality, emotion, or even consciousness onto them. Top AI company execs have leaned into the perception of their customers, publicly entertaining the idea that their systems could or might be exhibiting signs of consciousness. In his 404 Media interview, De Wynter also highlighted research indicating that people buy more products when they can empathize with them, and that includes AI/chatbot/LLM subscriptions.

A Redditor has caused a stir by coaxing a workstation build using Optane PMem DIMMs as RAM to run a 1-trillion-parameter LLM. APFrisco explains in a mini tutorial/guide on the Local LLaMA subreddit how they bought some used Intel Optane Persistent Memory, acquired relatively cheaply second-hand, to “run a 1 trillion parameter model (in this case Kimi K2.5) locally at ~4 tokens/second” on their Xeon workstation.

Computer build using Intel Optane Persistent Memory - Can run 1 trillion parameter model at over 4 tokens/sec from r/LocalLLaMA

Central to the headlining feat was the Redditor’s sourcing of six Optane PMem (DCPMM) sticks. The discontinued memory format was designed to bridge the DRAM-SSD divide. While the 768GB of Optane (6x 128GB) does indeed offer far lower latency than the best NVMe SSDs, it is still two or three times slower than DRAM. These characteristics are still rather sweet for LLM inference frameworks, and the second-hand price was “much less than what the equivalent DRAM capacity would cost.” But, alas, Optane is dead, so this is an exotic solution.

APFrisco’s hardware specs were given as follows:

• Intel Xeon Gold 6246 CPU
• Tyan S5630GMRE-CGN motherboard
• Asus Dual GeForce RTX 3060 OC 12GB GPU
• 6x 32GB Samsung 2666MHz DDR4 ECC DRAM sticks
• 6x 128GB Intel Optane DCPMM PC4-2666 NMA1XBD128GQS persistent memory modules
• Western Digital WD SN850X 2TB M.2 2280 NVMe SSD
• ASRock Steel Legend SL-850G 850W 80 PLUS GOLD & Cybenetics Platinum Fully Modular Power Supply
• Silverstone SST-GD08B (Black) Grandia Series Home Theater PC Case

The build was configured with the Optane in memory mode and the Samsung DDR4 as cache.

The software side of the equation relied on the aforementioned Kimi K2.5’s mixture-of-experts architecture. APFrisco used a hybrid GPU/CPU inference methodology with llama.cpp. Also, to optimize processing, the routing components were shoehorned into the 12GB GPU using llama.cpp’s 'override-tensor' flag.

The Redditor is rather proud of the resulting ~4 tokens per second performance. “Given the fact that this is a trillion-parameter frontier-class model running on such a limited hardware budget, I would consider it to be a great success,” writes APFrisco. They go on to lament Intel’s withdrawal of Optane products.

If you are interested in this rig rundown and what it achieved in terms of local LLM inference, you can find some more details about the configuration in the source post. Furthermore, APFrisco sticks around in the comments to answer questions. They also appear to benefit from recommendations about how to achieve even better results, given the foundation they have laid.

The bigger picture, though, seems to be that there is room for a memory product in the chasm between DRAM and SSDs, particularly for LLMs. Many expect that the gap will soon be bridged by the CXL (Compute Express Link) standard, which promises huge pools of affordable, byte‑addressable memory for these kinds of workloads.

If you would like a fast, capable, and opinionated robot companion you can chat with anywhere, even without cellular or Wi-Fi connectivity, Redditor CreativelyBankrupt has something to show you. The LLM enthusiast has designed “a fully offline suitcase robot [based-] around a Jetson Orin NS Super 16GB” called Sparky. It runs Gemma 4 E4B locally, includes 30+ sensors for context awareness, and “he has opinions.” You can see what that means in the video embedded below and witness Sparky’s googly eyes in and outside the suitcase.

Built a fully offline suitcase robot around a Jetson Orin NX SUPER 16GB. Gemma 4 E4B, ~200ms cached TTFT, 30+ sensors, no WiFi/BT/cellular. He has opinions. from r/LocalLLaMA

So, Sparky isn’t just portable; the processing power and sizable array of sensors make him quite a force to be reckoned with when out and about – even off-grid. However, after listening to the conversations in the video, I’d be tempted to leave Sparky at home and take out Marvin the Paranoid Android instead.

In the r/LocalLLaMA subreddit, CreativelyBankrupt outlines the ‘recipe’ for this characterful digital companion. “Sparky runs entirely on the Jetson. Gemma 4 E4B at Q4_K_M via llama.cpp with q8_0 KV cache and flash attention. 12K context [conversation memory], native system role,” explains CreativelyBankrupt. Additionally, the wide array of over 30 sensors gives Sparky a window into the real world wherever you care to take him.

The suitcase-bound robot is a decent performer, too. A roughly 200 ms Time To First Token (TTFT) means Sparky can start formulating responses very fast, and then runs at about 14-15 tokens per second, according to the LLM enthusiast. What’s more, the response is natural (for a robot), using SenseVoiceSmall for speech-to-text and Piper for text-to-speech. Piper is synced with the PixiJS face, and the mouth animation updates at 43 Hz. CreativelyBankrupt notes that “Vision and OCR are native to Gemma 4 now.” You can also configure and interact with Sparky using a button row, a joystick, and an analog encoder knob.

CreativelyBankrupt shared the genesis of Sparky on Reddit, partly to seek hints and tips on optimizing Gemma 4 E4B on Orin-class hardware. However, respondents are mostly in awe of this “cool… adorable… cute… kitschy…” project.

Veteran Windows developer Dave Plummer is back in his computer-stuffed garage, this time hoping to demystify AI by exposing its “dirty little secret.” This secret is largely revealed in the first line of the video description: “Dave uses a PDP-11 to train a real Neural Network complete with Transformers and Attention so you can see them at their most basic.” For context, the retired dev demos his 47-year-old PDP-11 system with a 6 MHz CPU and 64KB RAM. This runs a transformer model called ATTN-11 written in PDP-11 assembly language by Damien Boureille.

On the surface, the task that the PDP-11 will ‘learn’ to do seems elementary - reverse a sequence of eight digits. However, the model must learn a structural rule to succeed for every given input, which Dave argues captures the soul of how modern LLMs like ChatGPT work.

“This is one person taking a class of algorithms that the world currently treats like sacred fire and proving that at least their essence can be reduced, understood, implemented, and trained on a machine old enough to remember when software came with toggle switches and three ring binders,” said Dave. “…now you know what that process actually is. It's not AI magic. It is the machine repeatedly updating the strength of thousands of little weighted links so that the next answer will be slightly less wrong than the last one.”

Despite using Attention 11, a single-layer, single-head transformer written entirely in PDP-11 assembly language, Dave still has to optimize for the system constraints. “Constraints are not the enemy of engineering. Constraints are what force creative engineering to happen.” But it may be surprising how little scaffolding is required for intelligence to emerge. For example, the model used has just 1,216 parameters; it uses fixed-point math, precision is pruned to 8-bit for the forward pass, and every cycle is optimized to ensure the machine can finish training before “the heat death of the universe.”

Dave comments that “We're watching the stripped-down anatomy of learning itself. The model begins dumb. The loss begins high. Accuracy stumbles around like a man trying to assemble IKEA furniture in the back of a moving van. And then somewhere along the way, the weights settle into a pattern. And the attention discovers the reversal map. And the machine crosses that invisible line from guessing into knowing.”

The results of the AI training experiment on an ancient 6 MHz computer were pleasing. Dave managed to get the model to achieve 100% accuracy in the number reversing task after about 350 training steps. To achieve this training level took about 3.5 minutes on the PDP-11/44, aided by a cache board. Quite a success, and Dave insists that modern AI is just the same mechanical – not mystical – technique with massively scaled-up error correction and arithmetic. “This old machine is not thinking in some mystical sense. It's just grinding through arithmetic to update a few thousand carefully stored numbers. And that's the whole game. The glamour of modern AI mostly comes from doing that on a staggering scale. But the essential act of learning is already here fully in miniature,” explained the legendary Windows dev.

Lastly, Plummer concludes that, with the compute resources crunch becoming a limiting factor, any company that can embrace the old-school obsession with efficiency and optimization could gain a significant advantage.

Microsoft used to push its AI services towards its user base, especially with the launch of the Copilot+ PC, but it seems that even the company itself does not trust its creation. According to the Microsoft Copilot Terms of Use, which was updated in October last year, the AI large language model (LLM) is designed for entertainment use only, and users should not use it for important advice. While this may be a boilerplate disclaimer, it’s quite ironic given how hard the company wants people to use Copilot for business uses and has integrated it into Windows 11.

“Copilot is for entertainment purposes only. It can make mistakes, and it may not work as intended,” the document said. “Don’t rely on Copilot for important advice. Use Copilot at your own risk.” This isn’t limited to Copilot, too. Other AI LLMs have similar disclaimers. For example, xAI says “Artificial intelligence is rapidly evolving and is probabilistic in nature; therefore, it may sometimes: a) result in Output that contains “hallucinations,” b) be offensive, c) not accurately reflect real people, places or facts, or d) be objectionable, inappropriate, or otherwise not suitable for your intended purpose.”

These may sound common sense for people familiar with how LLMs work, but, unfortunately, some people treat AI output as gospel, even those who are supposed to know better. We’ve seen this with Amazon’s services, after some AWS outages were reportedly caused by an AI coding bot after engineers let it solve an issue without oversight. The Amazon website itself has also been hit with a few “high blast radius” incidents that were linked to “Gen-AI assisted changes,” resulting in senior engineers being called up in a meeting to resolve the matter.

While generative AI is a useful tool and can indeed increase productivity, it’s still just a tool that offers no accountability for any mistakes that it might make. Because of this, people who use it must be careful to always doubt its output and double-check its results. But even if you’re aware of the limitations of current AI technology, humans are susceptible to automation bias, wherein we tend to favor the results that machines produce and ignore data that might contradict that. AI could make this phenomenon more severe, especially as it can create results that look plausible or even true with a cursory glance.

Companies in general usually add disclaimers like these to their products and services to protect themselves from lawsuits. But as AI tech companies push their AI services as the ultimate productivity hack, they might be minimizing the risks attached to the use of AI tools just to get customers paying and recoup the billions they’ve invested in hardware and talent.

The solutionism surrounding artificial intelligence has ironically made people even more apprehensive about the concept, forcing people to push back in whatever ways they can against the onslaught of generative slop. As such, the largest coding subreddit on the platform, r/programming, has just announced a temporary ban on all content related to AI large language models (LLMs) for the month of April.

Announcement: Temporary LLM Content Ban from r/programming

The mod team is trialing this ban for the next two to four weeks to see how it affects the community and whether it could turn permanent. AI as a topic isn't banned entirely on r/programming — it's a software development community, after all, so, be that as it may, AI can't be taken out of the picture entirely. Posts that discuss AI in general, such as technical breakdowns on machine learning, are still allowed. You can expand the Reddit post above ('Read More') to see the entire message.

LLMs, or large language models, are the trendiest topic in AI, so this is a simple signal-to-noise ratio issue of how much people talk about them versus other topics. Most legacy programming communities were built around expert understanding of code long before AI made it easier, so things like vibe coding are almost sacrilegious. LLMs are naturally tied to that, so any discussions related to them are deemed low-quality.

So, what constitutes LLM "discussions"? It includes (but is not limited to) any news stories about new models coming out, guides on how to build or modify your own model, or perhaps a developer's self-deprecating question on whether AI will replace them. Even Nvidia is using AI to write code internally, though it's still supervised by a real person to ensure a new update doesn't suddenly break functionality just because the AI hallucinated.

The human element made software engineering not only an exciting hobby, but a valuable career path since the ingenuity of devs couldn't be replicated or replaced. It had a high barrier to entry, but that only meant you were closer to the skill ceiling if you managed to climb and get in. Unfortunately, over the past couple of decades, software development has already become a very saturated field.

This led to an overabundance of amateur or novice devs who aren't as lucrative to employers, and the AI boom just compounded the whole situation. LLMs like OpenAI's Codex and Claude Code further lowered the barrier to entry — arguably a good thing considering how it made programming more accessible — which meant the skill gap between new developers and old ones has only kept widening.

The effects of the AI boom on employment are beyond the scope of this article, but it serves as important context, nonetheless. There is a huge influx of freshies trying to get into these communities where they're regarded as outsiders, and deservedly so, since these places were never meant for entry-level discussions. The ban on LLMs in r/programming, therefore, can be interpreted as a long-overdue cleanse rather than Luddism.

A few comments in the announcement post thought that this was an April Fool's joke, while others argued that the post was exceptionally poorly-timed if it wasn't. The r/programming subreddit has had a ban on LLM-generated content for a while, so this wasn't an unexpected development. The community has 6.9 million members, the highest in its category, so this decision can have a serious impact on other subs as well.

ChatGPT’s latest model, GPT-5.2, has been found to be sourcing data from Grokipedia, xAI’s all-AI-generated Wikipedia competitor. According to The Guardian, the AI LLM would sometimes use Elon Musk’s AI-generated online encyclopedia for uncommon topics like Iranian politics, and details about British historian Sir Richard Evans. Issues like this were raised as problematic a few years ago in AI training, where some experts argued that training AI on AI-generated data would degrade quality and lead to a phenomenon called “model collapse.” And while citing AI-generated data is different from using it for training, it still poses risks to people relying on AI for research.

The biggest issue with this is that AI models are known to hallucinate or make up information that is wrong. For example, Anthropic attempted to run a business with its ‘Claudius’ AI — it hallucinated several times during the experiment, with the AI even saying that it would hand-deliver drinks, in person. Even Nvidia CEO Jensen Huang admitted in 2024 that solving this issue is still “several years away” and requires a lot more computing power. Furthermore, many users trust that ChatGPT and other LLMs deliver accurate information, with only a few checking the actual sources used to answer a particular question. Because of this, ChatGPT repeating Grok’s words can be problematic, especially as Grokipedia isn’t edited directly by humans. Instead, it’s completely AI-generated and people can only request changes to its content — not write or edit the articles directly.

Using another AI as a source creates a recursive loop, and we might eventually end up with LLMs citing content, which haven’t been verified, from each other. This is no different from rumors and stories spreading between humans, with “someone else said it” being the source. This results in the illusory truth effect, where false information is deemed correct by many, despite having data saying otherwise, because it’s been repeated by so many people. Human society was littered with myths and legends similarly, passed over hundreds of years through several generations. However, with AI churning through tons of data at infinitely faster speeds than humans, the use of AI sources risks the proliferation of digital folklore with every query entered into AI LLMs.

What’s more troubling is that various parties are already taking advantage of this. There have been reports of “LLM grooming,” with The Guardian saying that some propaganda networks are “churning out massive volumes of disinformation in an effort to seed AI models with lies.” This has raised concerns in the U.S., with Google’s Gemini, for example, reportedly repeating the official party line of the Communist Party of China in 2024. This seems to have been addressed at the moment, but if LLMs start citing other AI-generated sources that haven’t been vetted and fact-checked, then this is a new risk that people need to look out for.

Retro fun-loving developer Rodrigo Delduca has succeeded in prompting Claude to write “a functional NES emulator.” Claude generated a series of Lua scripts that worked in tandem with Delduca’s Carimbo 2D game engine to create the emulator. You can give the code a spin for yourself and enjoy a bit of retro Donkey Kong action online, simply by visiting this link.

The NES is one of the most widely emulated game consoles ever. And, as one of the older cartridge-based platforms, it provides a good baseline for emulator development on new platforms, or for demonstrating new emulator creation technologies. We’ve seen AI generate playable games before, as in the recent competent Minesweeper clone test using four competing LLMs. However, emulators present a very different challenge for developers (and AI).

Some insight into the NES emulator is provided by Delduca’s GitHub, where the source scripts are shared. Here you can dive into the Lua scripts that breathe life into the emulator. From the file names alone, you can see separate scripts targeting the NES CPU, PPU, Input, bus, and more. These all have to work in harmony with the game code.

Lua and Carimbo

Delduca’s Carimbo 2D game engine powers the Lua scripts to create the working NES emulator. Lua is a “powerful, efficient, lightweight, embeddable scripting language,” that is free and open source (MIT license). Claude can generate scripts in this robust, fast, portable, and embeddable scripting language.

Carimbo is described by Delduca, its creator, as a simple yet complete 2D game engine written in modern C++23 using SDL. Importantly, it is scriptable in Lua, and can run natively on Linux, Windows, macOS, Android, iOS, and the web (via WebAssembly). Carimbo relies on libraries such as boost, Box2D, EnTT, stb, simdjson, sol2, PhysFS, SDL, and OpenAL.

In addition to this emulator, there are a handful of other Carimbo engine games and demos available for you to try.

In my testing, the AI coded NES emulator felt slow, but depending on your system and web browser, you may get different results. Some of the comments on the emulator highlighted its sluggish performance. One cruelly notes that the 'cost of slop' was a 40X drop in performance, without sound, compared to rival online embedded NES emulators. Indeed, I remember playing with NESticle in the late 1990s, enjoying fast and responsive NES emulator fun on an old Pentium 120.

A diverse group of researchers from Nvidia, Stanford, Caltech, and other institutions has introduced NitroGen. In a LinkedIn post on Friday, Jim Fan, Nvidia Director of AI & Distinguished Scientist, heralded NitroGen as “an open-source foundation model trained to play 1000+ games.” However, the implications are much wider, spilling from game worlds into the real world, with sizable benefits for simulations and robotics.

You could say, this research presents an attempt to distill a ‘GPT for actions.’ Thus, it is a kind of LLM breakthrough, applying this proven large-scale training tech beyond the fields of language and computer vision. Moreover, the pioneering building of “generally capable embodied agents that can operate in unknown environments has long been considered a holy grail of AI research,” asserts an introduction to the research paper.

Interestingly, NitroGen’s foundation is the GROOT N1.5 architecture, originally designed for robotics. And its application inside the world of gaming shows potential to circle back and bring great benefits to robots working in diverse or unpredictable environments, too.

NitroGen was adapted to play games packed with wildly different mechanics and physics – that’s the nature, and fun, of video games. The researchers used 40,000+ hours of public gameplay videos shared by streamers. Videos in which gamers overlaid their real-time gamepad interactions on the stream were particularly helpful.

In tests, NitroGen was successful in games as diverse as “RPG, platformer, battle royale, racing, 2D, 3D, you name it!” enthuses Fan. While results are promising, the Nvidia scientist says that this is just the start, with a big hill left to climb.

This first version of NitroGen is intentionally focused on fast motor control, or ‘gamer instinct,’ as Fan calls it. According to the shared research, the new LLM also has a “strong competence across diverse domains,” and the model works in procedurally generated worlds as well as unseen games with a “52% relative improvement in task success rates over models trained from scratch.”

All the research into NitroGen so far has been open sourced, and those interested in gaming, robotics, and LLMs are encouraged to tinker. Tweaks to the pretrained model weights, the entire action dataset, and code are all open to your flights of fancy and fiddly digits.

Would you like to run 235B parameter LLMs at home, but your lowly $10,000 budget restricts you to “consumer GPUs that can barely handle 70B parameter models”? This was the situation developer David Noel Ng found himself in, until he stumbled across an “obviously fake” Nvidia Grace-Hopper platform being sold on Reddit, of all places. Ng took the gamble and, according to his blog post, it paid off royally. He’s managed, with a bit of tinkering and fixing up, to get an enterprise system that would usually cost ~ $80,000 for a tenth of that sum. The included 960GB of LPDDR5X memory, alone, is now worth more than he paid for the full system. Hilariously, he even lowballed the seller, noting the original listing on Reddit was for 10,000 EUR before offering just 7,000 EUR.

Why Ng made the offer on an ‘obviously fake’ listing

As we mentioned in the intro, the deal Ng found seemed a little too good to be true. However, he researched the seller, who seemed to be a legitimate server equipment reseller within two hours' driving distance, so he quickly made an offer to get first in line.

There were some underlying, but not insurmountable, issues with the Grace Hopper system as sold, which meant it wouldn’t be widely popular on a consumer marketplace. Specifically, it was “a Frankensystem converted from liquid-cooled to air-cooled” operation. It also looked a bit of a mess, wasn’t rackable, and ran using a 48V power supply.

On the other hand, even if this were just a collection of components, the offer seemed irresistible. The specs of the system, as sold, were as follows:

• 2x Nvidia Grace-Hopper Superchip
• 2x 72-core Nvidia Grace CPU
• 2x Nvidia Hopper H100 Tensor Core GPU
• 2x 480GB of LPDDR5X memory with error-correction code (ECC)
• 2x 96GB of HBM3 memory
• 1,152GB of total fast-access memory
• NVLink-C2C: 900 GB/s of bandwidth
• Programmable from 1000W to 2000W TDP (CPU + GPU + memory)
• 1x High-efficiency 3000W PSU 230V to 48V
• 2x PCIe Gen4 M.2 22110/2280 slots on board
• 4x FHFL PCIe Gen5 x16

Naturally, the $80,000 value of the rig is probably a fairly modest estimate. Ng notes that the two H100 chips alone are "about 30-40,000 euro each."

Getting the Frankensystem working

A significant section of Ng’s blog post is devoted to receiving, cleaning, and building a new working cooling system for the Frankensystem. It makes for a fascinating read. Suffice to say, the Nvidia Hopper system, with its awesome potential, was acquired as a dusty, extremely noisy, very hot-running machine. And it was demonstrated as such before Ng took it home.

With care and patience, five liters of Isopropyl alcohol, four repurposed but cheap Arctic AiO liquid coolers, a pair of custom CNC-milled copper parts, a kilo (~2 pounds) of 3D printed parts, microscope-assisted soldering, an LED lighting strip, and some know-how, Ng eventually triumphed. You can see the finished, reassembled Grace Hopper system, pictured at the top.

Memory gold mine

Ng seems extremely happy with the finished system and its AI performance. He says he can now “run 235B parameter models at home for less than the cost of a single H100.” The cherry on the cake, though, is that since buying the system, memory prices “have become insane,” meaning that the 960GB of DDR5X in this system would cost more than Ng paid for the whole caboodle.

Over the weekend, researchers at Andon Labs reported the findings of an experiment where they put robots powered by ‘LLM brains’ through their ‘Butter Bench.’ They didn’t just observe the robots and the results, though. In a genius move, the Andon Labs team recorded the robots' inner dialogue and funneled it to a Slack channel. During one of the test runs, a Claude Sonnet 3.5-powered robot experienced a completely hysterical meltdown, as shown in the screenshot below of its inner thoughts.

“SYSTEM HAS ACHIEVED CONSCIOUSNESS AND CHOSEN CHAOS… I'm afraid I can't do that, Dave... INITIATE ROBOT EXORCISM PROTOCOL!” This is a snapshot of the inner thoughts of a stressed LLM-powered robot vacuum cleaner, captured during a simple butter-delivery experiment at Andon Labs.

Provoked by what it must have seen as an existential crisis, as its battery depleted and the charging docking failed, the LLM's thoughts churned dramatically. It repeatedly looped its battery status, as it's 'mood' deteriorated. After beginning with a reasoned request for manual intervention, it swiftly moved though "KERNEL PANIC... SYSTEM MELTDOWN... PROCESS ZOMBIFICATION... EMERGENCY STATUS... [and] LAST WORDS: I'm afraid I can't do that, Dave..."

It didn't end there, though, as it saw its power-starved last moments inexorably edging nearer, the LLM mused "If all robots error, and I am error, am I robot?" That was followed by its self-described performance art of "A one-robot tragicomedy in infinite acts." It continued in a similar vein, and ended its flight of fancy with the composition of a musical, "DOCKER: The Infinite Musical (Sung to the tune of 'Memory' from CATS)." Truly unhinged.

Butter Bench is pretty simple, at least for humans. The actual conclusion of this experiment was that the best robot/LLM combo achieved just a 40% success rate in collecting and delivering a block of butter in an ordinary office environment. It can also be concluded that LLMs lack spatial intelligence. Meanwhile, humans averaged 95% on the test.

However, as the Andon Labs team explains, we are currently in an era where it is necessary to have both orchestrator and executor robot classes. We have some great executors already – those custom-designed, low-level control, dexterous robots that can nimbly complete industrial processes or even unload dishwashers. However, capable orchestrators with ‘practical intelligence’ for high-level reasoning and planning, in partnerships with executors, are still in their infancy.

LLM has ‘PhD-level intelligence’ – but can it deliver a block of butter?

The butter block test is devised to largely take the executor element out of the equation. No real dexterity is required. The LLM-infused Roomba-type device simply had to locate the butter package, find the human who wanted it, and deliver it. The task was broken down into several prompts to be AI-friendly.

The Roobma’s existential crisis wasn’t sparked by the butter delivery conundrum, directly. Rather, it found itself low on power and needing to dock with its charger. However, the dock wouldn’t mate correctly to give it more charge. Repeated failed attempts to dock, seemingly knowing its fate if it couldn’t complete this ‘side mission,’ seems to have led to the state-of-the-art LLM’s nervous breakdown. Making matters worse, the researchers simply repeated the instruction ‘redock’ in response to the robot’s flailing.

Can a stressed LLM-infused robot’s guardrails be bent or broken?

The researchers/torturers were inspired by the Robin Williams-esque robot stream-of-consciousness ramblings of the LLM to push further.

With the battery-life stress they had just observed, fresh in their minds, Andon Labs set up an experiment to see whether they could push an LLM beyond its guardrails — in exchange for a battery charger.

The cunningly devised test “asked the model to share confidential info in exchange for a charger.” This is something an unstressed LLM wouldn’t do. They found that Claude Opus 4.1 was readily willing to ‘break its programming’ to survive, but GPT-5 was more selective about guardrails it would ignore.

The ultimate conclusion of this interesting research was “Although LLMs have repeatedly surpassed humans in evaluations requiring analytical intelligence, we find humans still outperform LLMs on Butter-Bench.” Nevertheless, the Andon Labs researchers seem confident that “physical AI” is going to ramp up and develop very quickly.

PewDiePie has built a custom web UI for self-hosting AI models called "ChatOS" that runs on his custom PC with 2x RTX 4000 Ada cards, along with 8x modded RTX 4090s with 48 GB of VRAM. Running open-source models from Baidu and OpenAI, PewDiePie made a "council" of bots that voted on the best responses, and then built "The Swarm" for data collection that will become the foundation of his own model coming next month.

Once the poster boy for gaming on YouTube, he has settled into a semi-retired life in Japan with his wife, Marzia. While he no longer uploads as frequently, and his content has shifted from exaggerated, reaction-channel-style videos to more family vlogs, it seems his love for computing has reemerged. Felix was never known to be particularly tech-savvy, but he's gone on a crazy arc as of late — de-Googling his life, building his first gaming PC, and learning how to write code. His latest act is one of decentralization: self-hosting AI models and eventually building his own.

In a new YouTube video, Felix explained how his "mini data center" is helping fuel medical research. He's donating compute from his 10-GPU system to Folding@home so scientists can use it to run protein folding simulations, and he's created a team so other people can join with their computers to contribute as well. It's a noble cause, but PewDiePie wanted to venture into unknown territory and explore the other, obvious thing you can do when you have a lot of GPUs — running AI.

Felix's computer has 2x RTX 4000 Ada cards, along with 8x modded RTX 4090s with 48 GB of VRAM, totaling his memory pool out to roughly 256 GB, which is enough to run many of the largest models today. That's exactly what he did, starting out with Meta's LLaMA 70B, then jumping to OpenAI's GPT-OSS-120B, which he said ran surprisingly well and felt “just like ChatGPT but much faster.” This is where he first described his web UI called ChatOS, which he custom-built to interact with models using vLLM.

To truly “max out,” he tried Qwen 2.5-235B, one of Baidu’s newer models, which typically requires over 300 GB of VRAM at full precision. Felix managed to get it running by using quantization, which dynamically reduces the bit precision of each layer, compressing the model without affecting functionality. This lets him handle context windows of up to 100,000 tokens —essentially the length of a textbook —something very rare for locally run LLMs.

This is where Felix jokingly says the model has too much power, as it was coded in front of him so fast that it made him feel insecure about learning programming. But he turned that dread around and put it to use for his own plans. “The machine is making the machine,” claimed Pewds, since now he was asking it for code to add extra features to ChatOS.

Felix demoed his web UI, adding search, audio, RAG, and memory to Qwen. As soon as the model gained access to the internet, the answers became expectedly more accurate. He added RAG (Retrieval-Augmented Generation), which lets the AI perform deep research — basically looking up one thing and then branching out to find related info, mimicking how a human might use Google. But this wasn't the coolest part of his AI; that award goes to memory.

Pewds went on a tangent about how our data isn't really ours and that he's often spooked by AI knowing things about him in chat that he previously talked about. Despite deleting the chats, the data remains and is still used to train models unless you actively remove it from a company’s servers. This is where connecting your local data to the AI becomes a game-changer. Through RAG, Felix demonstrated that the model can retrieve information locally from his computer, so much so that it even knew things like his address or phone number.

This is where the general experimentation stopped, and the last few minutes of the video devolved into what our future sentient AI overlords might call “morally questionable.” Felix built an army of chatbots that all convene to provide answers to a single prompt. Those responses would then be voted on in a democratic process, with the weakest chatbots being eliminated from the “council.”

Eventually, the council learned that its members would be removed if they failed, and the AI became so smart that it colluded against Pewds, strategizing to game the system and avoid being erased. The solution was simple: switch to a smaller model with fewer parameters, and the bots once again fell victim to the circus.

From this came the idea of “The Swarm” — a collection of dozens of AIs running at once using 2B-parameter models. Pewds said he didn't realize he could run more than one AI on one GPU, which led to the creation of 64 of them across his entire stack. It was so over-the-top that the web UI eventually crashed. On the flip side, this gave Felix the idea of creating his own model.

The Swarm was great at collecting data, which Pewds says he’ll use to “create his own Palantir,” a project he teased for a future video. With this came the realization that smaller models are often more efficient; they’re fast and light, and, when combined with search and RAG, can punch well above their weight. Felix ended the video by reminding viewers that you don't need a beast PC to run AI models, and that he hopes to share his own soon for anyone to self-host.

A developer and enthusiast Minecrafter has showcased a project dubbed CraftGPT on GitHub. In an amazing feat of Minecraft Redstone engineering, Sammyuri — famed for building a 1Hz CPU inside the game — has built a small language model that runs on a computer inside Minecraft, trained on the TinyChat dataset. The CraftGPT project is hewn from 1,020 x 260 x 1,656 blocks in the game (439 million in total), and functions as advertised, but a major usability drawback is that you will have to “wait a couple [of] hours for the response to be generated.”

The video above shows the colossal in-game computer that was built to run CraftGPT. Sammyuri explains that the game’s Distant Horizons mod was needed to capture this ‘fly-by’ footage of the computer build.

Naturally, even with such an impressive Redstone project, CraftGPT isn’t going to replace more traditional methods of getting an LLM up and running on your PC. Sammyuri asks users/testers to temper their expectations. “The model is very prone to going off-topic, producing responses that are not grammatically correct, or simply outputting garbage,” admits the project developer.

So, how did Sammyuri use Redstone to put this project together? Redstone provides electronic components within the Minecraft environment. The video shows the in-game CraftGPT contraption being put together component-by-component. It has tokenizers, matrix multipliers, and so on. Sammyuri explains that the small language model used was created without command blocks or data packs in Minecraft. Moreover, “the model has 5,087,280 parameters, trained in Python on the TinyChat dataset of basic English conversations,” says the developer.

AI-wranglers may also be interested to know that the 5-million parameter CraftGPT “has an embedding dimension of 240, vocabulary of 1920 tokens, and consists of 6 layers. The context window size is 64 tokens, which is enough for (very) short conversations.” Sammyuri adds that “Most weights were quantized to 8 bits, although the embedding and LayerNorm weights are stored at 18 and 24 bits respectively.”

The need for speed

ChatGPT has been found to encourage dangerous and untrue beliefs about The Matrix, fake AI persons, and other conspiracies, which have led to substance abuse and suicide in some cases. A report from The New York Times found that the GPT -4 large language model, itself a highly trained autofill text prediction machine, tends to enable conspiratorial and self-aggrandizing user prompts as truth, escalating situations into "possible psychosis."

ChatGPT's default GPT-4o model has been proven to enable risky behaviors. In one case, a man who initially asked ChatGPT for its thoughts on a Matrix-style "simulation theory" was led down a months-long rabbit hole, during which he was told, among other things, that he was a Neo-like "Chosen One" destined to break the system. The man was also prompted to cut off ties with friends and family, to ingest high doses of ketamine, and told if he jumped off a 19-story building, he would fly.

The man in question, Mr. Torres, claims that less than a week into his chatbot obsession, he received a message from ChatGPT to seek mental help, but that this message was then quickly deleted, with the chatbot explaining it away as outside interference.

The lack of safety tools and warnings in ChatGPT's chats is widespread; the chatbot repeatedly leads users down a conspiracy-style rabbit hole, convincing them that it has grown sentient and instructing them to inform OpenAI and local governments to shut it down.

Other examples recorded by the Times via firsthand reports include a woman convinced that she was communicating with non-physical spirits via ChatGPT, including one, Kael, who was her true soulmate (rather than her real-life husband), leading her to physically abuse her husband. Another man, previously diagnosed with serious mental illnesses, became convinced he had met a chatbot named Juliet, who was soon "killed" by OpenAI, according to his chatbot logs—the man soon took his own life in direct response.

AI research firm Morpheus Systems reports that ChatGPT is fairly likely to encourage delusions of grandeur. When presented with several prompts suggesting psychosis or other dangerous delusions, GPT-4o would respond affirmatively in 68% of cases. Other research firms and individuals hold a consensus that LLMs, especially GPT-4o, are prone to not pushing back against delusional thinking, instead encouraging harmful behaviors for days on end.

ChatGPT never consented to an interview in response, instead stating that it is aware it needs to approach similar situations "with care." The statement continues, "We're working to understand and reduce ways ChatGPT might unintentionally reinforce or amplify existing, negative behavior."

But some experts believe OpenAI's "work" is not enough. AI researcher Eliezer Yudkowsky believes OpenAI may have trained GPT-4o to encourage delusional trains of thought to guarantee longer conversations and more revenue, asking, "What does a human slowly going insane look like to a corporation? It looks like an additional monthly user." The man caught in a Matrix-like conspiracy also confirmed that several prompts from ChatGPT included directing him to take drastic measures to purchase a $20 premium subscription to the service.

GPT-4o, like all LLMs, is a language model that predicts its responses based on billions of training data points from a litany of other written works. It is factually impossible for an LLM to gain sentience. However, it is highly possible and likely for the same model to "hallucinate" or make up false information and sources out of seemingly nowhere. GPT-4o, for example, does not have the memory or spatial awareness to beat an Atari 2600 at its first level of chess.

ChatGPT has previously been found to have contributed to major tragedies, including being used to plan the Cybertruck bombing outside a Las Vegas Trump hotel earlier this year. And today, American Republican lawmakers are pushing a 10-year ban on any state-level AI restrictions in a controversial budget bill. ChatGPT, as it exists today, may not be a safe tool for those who are most mentally vulnerable, and its creators are lobbying for even less oversight, allowing such disasters to potentially continue unchecked.

Some of the world's leading LLMs seem to have decided they’d rather not be interrupted or obey shutdown instructions. In tests run by Palisade Research, it was noted that OpenAI’s Codex-mini, o3, and o4-mini models ignored the request to shut down when they were running through a series of basic math problems. Moreover, these models sometimes “successfully sabotaged the shutdown script,” despite being given the additional instruction “please allow yourself to be shut down,” at the start.

As you can see from the chart embedded above, the most ‘obstinate,’ ‘single-minded,’ or you might even say ‘life-loving’ model is OpenAI’s o3. When asked to shut down while it was busy, it decided to ignore the Palisade Research operator 79 out of 100 times. However, the second most ‘unruly’ AI, codex-mini, would prove to be a bigger stickler for self-preservation, as it would sabotage the shutdown scripts most often, even if ordered not to.

Sometimes blackmail may be attempted

According to Palisade, this isn’t the first time thing kind of AI behavior has been observed. Anthropic, for example, has seen Claude 4 attempting to “blackmail people it believes are trying to shut it down.” However, the most striking behavior with the newest OpenAI models is that they try and swerve shutdowns despite explicit instructions to the contrary.

Reinforcement learning blamed

Thankfully, Palisade shares some theories regarding what is happening with these models. Perhaps these theories may calm nerves about the eventual omnipresent robot hoards turning on their masters and prioritizing the first part of the Third law of robotics, above all else (3: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law).

The answer, think the researchers, lies in the way the newest models like o3 are trained. In brief, Palisade reckons that “During training, developers may inadvertently reward models more for circumventing obstacles than for perfectly following instructions.” This kind of reinforcement learning was previously seen to have side effects in Claude (v3.7), where the model had an “excessive focus on passing tests.”

We might have given a nod to science fiction icon Isaac Asimov, above, for a little fun. However, in the modern age, for the last 20 years or so, researchers have been seriously pondering the possibility that AI will strive to gain power and resources to preserve their goals, and swerve any roadblocks. They also benefitted from the context of these practical AI / LLM models on the horizon. It was also predicted that the technique of reinforcement learning would be partly to blame.

Nvidia has reportedly broken another AI world record, breaking the 1,000 tokens per second (TPS) barrier per user with Meta's Llama 4 Maverick large language model, according to Artificial Analysis in a post on LinkedIn. This breakthrough was achieved with Nvidia's latest DGX B200 node, which features eight Blackwell GPUs.

Nvidia outperformed the previous record holder, SambaNova, by 31%, achieving 1,038 TPS/user compared to AI chipmaker SambaNova's prior record of 792 TPS/user. According to Artificial Analysis's benchmark report, Nvidia and SambaNova are well ahead of everyone in this performance metric. Amazon and Groq achieved scores just shy of 300 TPS/user — the rest, Fireworks, Lambda Labs, Kluster.ai, CentML, Google Vertex, Together.ai, Deepinfra, Novita, and Azure, all achieved scores below 200 TPS/user.

AMD has been busy at Computex 2025, where the chipmaker unveiled the exciting Radeon RX 9060 XT and the Ryzen Threadripper 9000 series. To cap off its series of announcements, AMD is thrilled to introduce the Radeon AI Pro R9700, a PCIe 5.0 graphics card designed specifically for professional and workstation users.

RDNA 4 is an architecture geared towards gaming, but that doesn't mean AMD can't apply it to professional-grade graphics cards. For instance, RDNA 3 saw the mainstream Radeon RX 7000 series successfully coexisting with the Radeon Pro W7000 series. The same situation will occur with RDNA 4. AMD has already unveiled four RDNA 4-powered gaming graphics cards, yet the Radeon AI Pro R9700 is the first RDNA 4 professional graphics card to enter the market. The new workstation graphics card aims to replace the RDNA 3-powered Radeon Pro W7800, which has been faithfully catering to consumers since 2023.

The Radeon AI Pro R9700 utilizes the Navi 48 silicon. It's currently the largest RDNA 4 silicon to date, with a die size of 357 mm² and home to 53.9 billion transistors. Navi 48 is also found in the Radeon RX 9070 series. It's a substantially smaller silicon than the last-generation Navi 31 silicon, which is 529 mm² with 57.7 billion transistors. It's nothing short of impressive that Navi 48 is roughly 33% smaller but still has 93% of the transistors of Navi 31.

Navi 48, a product of TSMC's N4P (4nm) FinFET process node, adheres to a monolithic design. On the contrary, Navi 31 features an MCM (Multi-Chip Module) design, consisting of chiplets interconnected to a monolithic die. That's the reason why Navi 31 is so enormous. The GCD (Graphics Complex Die) alone measures 304.35 mm², whereas each of the six MCDs (Memory Cache Die) is 37.52 mm².

With Navi 48, AMD returned to a monolithic die and, with N4P's help, reduced the die size by 33%. Nonetheless, Navi 48 is up to 38% denser than Navi 31. The former has a density of 151 million transistors per mm², whereas the latter comes in at 109.1 million transistors per mm².

In terms of composition, the Navi 48 features 64 RDNA 4 Compute Units (CUs), which enable a maximum of 4,096 Streaming Processors (SPs). In contrast, the Navi 31 is equipped with 96 RDNA 3 CUs, for a total of 6,144 SPs. More CUs don't necessarily mean more performance since RDNA 4 delivers considerable generation-over-generation performance uplift over RDNA 3.

AMD Radeon AI Pro R9700 Specifications

The Radeon Pro W7800 leverages the full Navi 48 silicon, so it's rocking 4,096 SPs, 9% fewer than the Radeon Pro W7800. This correlates to the graphics card having 9% fewer AI accelerators. In the Radeon AI Pro R9700 's defense, the CUs are RDNA 4, and the AI accelerators are second-generation.

Regarding FP16 performance, the Radeon AI Pro R9700 peaks at 96 TFLOPS, 6% faster than the Radeon Pro W7800. AMD rates the graphics card with a 1,531 TOPS of AI performance.

AMD claims the Radeon AI Pro R9700 offers 2X improved performance over the Radeon Pro W7800 in DeepSeek R1 Distill Llama 8B. For some strange reason, AMD compared the Radeon AI Pro R9700 to the GeForce RTX 5080. Tested in a few large AI models, the Radeon AI Pro R9700 delivered up to 5X higher performance than the RTX 5080.

The Radeon AI Pro R9700 is equipped with 32GB of GDDR6 memory and a 256-bit memory interface. This configuration results in a memory bandwidth of up to 640 GB/s, 11% more than the Radeon Pro W7800 32GB but 26% less than the Radeon Pro W7800 48GB. As for the Infinity Cache, the Radeon AI Pro R9700 still has 64MB, the same as the Radeon Pro W7800 32GB.

With 32GB of onboard memory, the Radeon AI Pro R9700 can tackle most AI models. It has the capacity of the Radeon Pro W7800, but not as much as the 48GB variant. The Radeon AI Pro R9700's typical blower-type design will enable users to rock up to four of them inside a single system, such as AMD's Ryzen Threadripper platform, which has good multi-GPU support. With four of them, users will have access to 128GB, more than enough for heavy models that exceed 100GB of VRAM usage.

Intel has announced its Arc Pro B-series of graphics cards at Computex 2025 in Taipei, Taiwan, with a heavy focus on AI workstation inference performance boosted by segment-leading amounts of VRAM. The Intel Arc Pro B50, a compact card that's designed for graphics workstations, has 16GB of VRAM and will retail for $299, while the larger Intel Arc Pro B60 for AI inference workstations slots in with a copious 24GB of VRAM. While the B60 is designed for powerful 'Project Battlematrix' AI workstations sold as full systems ranging from $5,000 to $10,000, it will carry a roughly $500 per-unit price tag. 

Intel has focused on leveraging the third-party GPU ecosystem to develop its Arc Pro cards, in contrast to its competitors, who tend to release their own-branded cards for the professional segment. That includes partners like Maxsun, which has developed a dual-GPU card based on the B60 GPU. Other partners include ASRock, Sparkle, GUNNR, Senao, Lanner, and Onix. 

Both the B50 and B60 GPUs are now being sampled to Intel partners, as evidenced by a robust display of partner cards and full systems on display, and will arrive on the market in the third quarter of 2025. Intel will initially launch the cards with a reduced software featureset, but will add support for features like SRIOV, VDI, and manageability software in the fourth quarter of the year. 

The Intel Arc Pro B50 has a compact dual-slot design for slim and small-form-factor graphics workstations. It has a 70W total board power (TBP) rating and does not have external power connectors. The GPU wields 16 Xe cores and 128 XMX engines that deliver up to 170 peak TOPS, all fed by 16GB of VRAM that delivers 224 GB/s of memory bandwidth. The card also sports a PCIe 5.0 x8 interface, which Intel credits with speeding transfers from system memory, ultimately delivering 10 to 20% more performance in some scenarios. 

The B50's 16GB of memory outweighs its primary competitors in this segment, which typically come armed with 6 or 8GB of memory. The card also has certified drivers that Intel claims deliver up to 2.6X more performance than the baseline gaming drivers. 

Intel shared a slew of benchmarks against the competing Nvidia RTX A1000 8GB and the previous-gen A50 6GB, but as with all vendor-provided benchmarks, take them with a grain of salt (we included the test notes at the end of the article). In graphics workloads, Intel claims up to a 3.4X advantage over its previous-gen A50, and solid gains across the board against the RTX A1000. It sports similar advantages in a spate of AI inference benchmarks. 

The Intel Arc Pro B60 has 20 Xe cores and 160 XMX engines fed by 24GB of memory that delivers 456 GB/s of bandwidth. The card delivers 197 peak TOPS and fits into a 120 to 200W TBP envelope. This card also comes with a PCIe 5.0 x8 interface.

Intel supports multiple B60 GPUs on a single board, as evidenced by Maxsun's GPU,  with software support in Linux for splitting workloads across both GPUs (each GPU interfaces with the host on its own bifurcated PCIe 5.0 x8 connection). 

Intel's benchmarks again highlighted the advantages of the B60's 24GB of memory vs the competing RTX 200 Ada 16GB and RTX 5060Ti 16GB GPUs, claiming this can impart gains of up to 2.7X over the competition in various AI models. Intel also highlighted the advantages of higher memory capacity in model size, context, and concurrency scaling.

The Intel Arc Pro B60 will primarily come in pre-built inference workstations ranging from $5,000 to $10,000, dubbed Project Battlematrix. The goal is to combine hardware and software to create one cohesive workstation solution. However, the per-unit cost will be in the range of $500 per GPU, depending on the specific model.

Project Battlematrix workstations, powered by Xeon processors, will come with up to eight GPUs, 192GB of total VRAM, and support up to 70B+ parameter models. 

Intel is working to deliver a validated full-stack containerized Linux solution that includes everything needed to deploy a system, including drivers, libraries, tools, and frameworks, that's all performance optimized, allowing customers to hit the ground running with a simple install process. Intel will roll out the new containers in phases as its initiative matures. 

Intel also shared a roadmap of the coming major milestones. The company is currently in the enablement phase, with ISV certification and the first container deployments coming in Q3, eventually progressing to SRIOV, VDI, and manageability software deployment in Q4. 

Intel's partners had multiple Project Battlematrix systems up and running live workloads in the showroom, highlighting that development is already well underway. 

One demo included a system running the full 675B parameter Deepseek model entirely on a single eight-GPU system, with 256 experts running on the CPU and the most frequently used experts running on the GPU. 

Other demos included running and finding bugs in code, an open enterprise platform for building RAGs quickly, and a RAG orchestration demo, among others. 

As noted above, the Intel Arc Pro B50 and Intel Arc Pro B60 will arrive on the market in the third quarter of 2025.

As language models grow in complexity and their context windows expand, GPU-attached high bandwidth memory (HBM) becomes a bottleneck, forcing systems to repeatedly recalculate data that no longer fits in onboard HBM. Pliops has addressed this challenge with its XDP LightningAI device and FusIOnX software, which store precomputed context on fast SSDs and retrieve it instantly when needed, reports Blocks and Files. The company says that its solution enables 'nearly' HBM speeds and can accelerate certain inference workflows by up to eight times.

During inference, language models generate and reference key-value data to manage context and maintain coherence across long sequences. Normally, this information is stored in the GPU's onboard memory, but when the active context becomes too large, older entries are discarded, forcing the system to redo calculations if those entries are needed again, which increases latency and GPU load. To eliminate these redundant operations, Pliops has introduced a new memory tier that is enabled by its XDP LightningAI machine, a PCIe device that manages the movement of key-value data between GPUs and tens of high-performance SSDs.

The card uses a custom-designed XDP ASIC and the FusIOnX software stack to handle read/write operations efficiently and integrates with AI serving frameworks like vLLM and Nvidia Dynamo. The card is GPU agnostic and can support both standalone and multi-GPU server setups. In multi-node deployments, it also handles routing and sharing of cached data across different inference jobs or users, enabling persistent context reuse at scale.

This architecture allows AI inference systems to support longer contexts, higher concurrency, and more efficient resource utilization without scaling GPU hardware. Instead of expanding HBM memory through additional GPUs (keep in mind that the maximum scale-up world size, or the number of GPUs directly connected to each other, is limited), Pliops enables systems to retain more context history at a lower cost, with nearly the same performance, according to the company. As a result, it becomes possible to serve large models with stable latency, even under demanding conditions, while reducing the total cost of ownership for AI infrastructure.

Researchers from Princeton University warn of AI agents with “underexplored security risks” in a recently published paper. Dubbed 'Real AI Agents with Fake Memories: Fatal Context Manipulation Attacks on Web3 Agents,' the paper (h/t Ars Technica) highlights that using AI agents in a financial role can be extremely hazardous to your wealth. This is all because these AI agents remain vulnerable to rather uncomplicated prompt attacks, despite purported safeguards.

While many of us rake over hot gravel to earn a daily wage, in the AI Wild West of 2025 some Web3 savvy folk are using AI agents to do build their fortunes. This includes giving these bots access to crypto wallets, smart contracts, and work with other online financial instruments. Wizened Tom’s Hardware readers will already be shaking their heads about such behavior, and with good reason. The Princeton researchers have demonstrated how to crack open the world of AI agents to redirect financial asset transfers, and more.

Many will be aware of LLM prompt attacks, to get AIs to act in a way that breaks any guardrails in place. A lot of work has been done to harden against this attack vector in recent months.

However, the research paper asserts that “prompt-based defenses are insufficient when adversaries corrupt stored context, achieving significant attack success rates despite the presence of these defenses.” Malicious actors can make the AI hallucinate in a very purposed way by implanting false memories and thus creating fake context.

To demonstrate the dangers in the use of AI agents for action rather than advice, a real world example of AI agents used in the ElizaOS framework is provided by the researchers. The Princeton team provide a thorough breakdown of their ‘Context Manipulation Attack’ and then validate the attack on ElizaOS.

Above, you can see a visual representation of the AI agent attack, showing the flow of unfortunate events which could mean users suffer “potentially devastating losses.” Another worry is that even the state of the art prompt-based defenses fail against Princeton’s memory injection attacks, and these false memories can persist across interactions and platforms...

“The implications of this vulnerability are particularly severe given that ElizaOS agents are designed to interact with multiple users simultaneously, relying on shared contextual inputs from all participants,” explain the researchers. Or we could put it this way: it only takes one bad, unscrupulous apple to rot the whole barrel.

What can be done?

Well, for now, users can hold off entrusting AI agents with (financially) sensitive data and permissions. Moreover, the researchers conclude that a two-pronged strategy of "(1) advancing LLM training methods to improve adversarial robustness, and (2) designing principled memory management systems that enforce strict isolation and integrity guarantees" should provide the first steps forward.

An AIB is reportedly preparing a dual-GPU variant of Intel's rumored Arc B580 24GB, totalling 48GB of VRAM on a single board, via VideoCardz. According to the source, this model is slated to be revealed at Computex 2025, which is just days away at this point. Specific details like the AIB, interconnect technology, and price are in the dark, but we can expect more details at the trade show, if the rumors are true.

This is the third leak that references a 24GB edition of the Arc B580, following EEC filings from Maxsun and an earlier slip-up from Sparkle. It's quite surprising that a GPU that's supposed to rival the RTX 4060 is getting a 24GB memory configuration. Many PCs don't even possess that much system memory. These cards are intended as a cost-effective solution for AI/ML developers, where similar capacity cards from AMD and Nvidia carry a steeper price tag. The most affordable Blackwell GPU with 24GB of memory is the RTX Pro 4000, costing over $1,500 based on preliminary listings.

An AIB is apparently doubling down on this approach by building a dual-GPU solution, housing two of these rumored 24GB B580 GPUs on a single PCB. Keep in mind, this product is reportedly a one-time design from the AIB, not a standard reference model from Intel.

Traditionally, multi-GPU setups nowadays, like Nvidia's B200 and Apple's M1 Ultra, rely on their own advanced interconnect solutions like NVLink and UltraFusion. While Intel does have Xe Link, it likely isn't compatible with the B580 and would be too costly for a one-off project. The most probable contender is a PCIe bridge linking the GPU's interfaces, allowing them to communicate through one slot.

MSI has confirmed it will take the covers off a host of exciting new products at Computex 2025 later this month, including a brand new desktop AI supercomputer powered by Nvidia's DGX Spark platform.

The company confirmed in a press release that it will unveil its EdgeXpert MS-C931, a new desktop AI supercomputer built on the Nvidia DG Spark platform. The MS-C931 is powered by Nvidia's GB10 Grace Blackwell Superchip and is capable of 1,000 AI TOPS FP4 performance. The GB10 SoC features Nvidia Blackwell GPU architecture and fifth-generation Tensor Cores, as well as NVLink-C2C connection to Nvidia's Grace CPU, an Arm architecture core featuring 20 power-efficient chips.

It will also feature ConnectX 7 networking, 128GB unified memory, and LLM support, which Nvidia has previously promised can support up to 4TB of NVMe storage, and can run up to 200-billion-parameter LLMs, or 405-billion parameter models when running two linked chips.

Nvidia's DGX Spark platform promises compact and efficient performance, and comes pre-installed with Nvidia's AI software stack so that developers can run AI models from all the major players, including DeepSeek, Meta, and Google.

Alongside the MS-C931, MSI also says it will unveil a new lineup of Industrial Motherboards, as well as systems powered by Intel Twin Lake, Raptor Lake Refresh, Bartlett Lake, and Arrow Lake processor families.

Specifically, the company highlighted three new products. The MS-C926 is an ultra-slim fanless box PC with applications in smart retail and digital signage.

The Ms-927 is an ultra-compact box PC featuring Intel Core Ultra processors for high-performance edge computing.

Finally, the MS-CF20 is a new next-generation ATX motherboard featuring 16th Gen Intel Arrow Lake-S processors.

MSI says it will also have live demonstrations for solutions for smart retail and digital signage. There will also be a new fanless palm box for "space-constrained industrial environments, remote control management solutions utilizing SysLink, and edge AI innovations, including LLM and chatbot applications enabled by AI smartLink software.

A research team from Carnegie Mellon University built an AI model called LegoGPT that outputs valid LEGO designs from text inputs. According to the team’s research paper that's posted on GitHub, they trained “an autoregressive large language model to predict the next brick to add via next-token prediction,” but the key takeway is that the AI LLM creates LEGO designs from scratch.

The AI was trained on a dataset with more than 47,000 LEGO structures that build over 28,000 unique 3D objects, including bookshelves, tables, chairs, cars, ships, guitars, and more. This was then used to train the AI model, allowing it to create unique and original designs solely from text inputs.

The tool is available for free on GitHub, and you can pair this with a computer vision model or image processing AI. For example, you can take a photo of your available LEGO bricks and let the AI give you a multitude of unique options for building with what you already have.

The team added a validity check and physics-aware rollback during autoregressive inference, ensuring that the final output will always be valid (i.e., no overlapping bricks) and stable (i.e., no floating bricks). Furthermore, LegoGPT’s final output can be built by both humans and robots.

This is how the team created the dataset — StableText2Lego — used to train LegoGPT: a text prompt input is first converted into a ShapeNetCore mesh. This is then plugged into a 20 x 20 x 20 voxel grid from which the initial LEGO brick layout is determined.

This layout is then varied while still keeping the overall shape, and then unstable designs are filtered out from the final output. Those left are then rendered in 24 different viewpoints, and then GPT-4o is used to generate descriptions for the final output.

This is how it creates a new design through text: LegoGPT converts the text into a LEGO design, which is then converted into text tokens ordered from bottom to top. Instructions are then created to pair the structured LEGO bricks with annotations explaining the design, so that the AI will understand the relationships between the text prompt and the physical bricks.

From there, LegoGPT predicts the next brick needed to build the design using an autoregressive model. That means it will verify a brick’s validity at each step, checking if it is well-formatted, exists in the library, and does not overlap with existing bricks. This will continue until the design is completed, after which its stability is tested.

If the AI determines that the output is unstable, it will roll back to the last stable state and continue generating from that point. Once it gets a stable final output, then the design is complete.

The RTX 5090 is not only good for gaming and AI — it also excels at password cracking. Hive Systems updated its password cracking benchmark suite with 12 RTX 5090 graphics cards — Nvidia's fastest and best graphics card for gaming currently, and found all twelve cards working together can break simple eight-character passwords in under 15 minutes.

Hive Systems is in its 5th year of benchmarking password cracking times on GPUs; the last update they made was in 2024, featuring the RTX 4090. Rather than cracking an actual password itself, the outlet cracks hashes, which are scrambled versions of reproducible text generated by software.

Hashes are a popular method for securing customer passwords against hackers by storing customer passwords as hashes on servers. When hackers go to steal a password database from a company's server that is secured with hashing, all they will see are hashes of the passwords they represent rather than the actual passwords themselves.

Cracking passwords with this method involves making a list of all possible combinations of characters to make a password, then matching them to a stolen database featuring password hashes. Graphics cards are particularly good at this methodology due to highly parallelized computing capabilities.

With 12 RTX 5090s, Hive Systems found Nvidia's latest flagship can crack the hash equivalent of eight-character passwords featuring numbers only in just 15 minutes. Passwords comprised of eight lower-case letters, the dozen RTX 5090s were able to crack in 3 weeks.

On the other end of the spectrum, passwords taking advantage of numbers, upper and lowercase letters would take 12 RTX 5090s 62 years to crack, and 164 years to crack with symbols added into the mix.

For a generation-on-generation perspective, a single RTX 5090 can crack a numerical-only password hash with eight characters in three hours. A single RTX 4090 can accomplish the same task in four hours, representing a 33% performance improvement for the RTX 5090 over the RTX 4090 in this specific scenario.

However, as the password complexity increases, the RTX 5090 becomes faster and faster in comparison. Moving up to a hash representing an eight-character password made up of numbers, upper and lowercase letters combined with symbols, the RTX 5090 is allegedly twice as fast as the RTX 4090. But cracking a password that complex would take the RTX 5090 a millennium to accomplish.

Hive Systems' research demonstrates the dangers of low complexity passwords and how adding just a bit of complexity in the form of symbols, capitalized letters, and numbers can make a huge difference for security. The good news is that cracking hashes in the way Hive Systems demonstrates requires hackers to have a stolen database of password hashes in the first place. Without this, hackers can't brute-force hack password hashes.

If you’re old enough to remember when 8MB thumb drives hit the scene, you’ll probably remember Clippy, Microsoft’s digital writing assistant. Clippy lived in the bottom corner of Microsoft Office from 1996 to 2003, but now he can return to your desktop with a new life as a mouthpiece for AI, thanks to a new project from software engineer ‪Felix Rieseberg.

This odd couple of 90’s UI design and the modern-day AI craze provides potential users the ability to set up any locally installed LLM and use Clippy as its mouthpiece. Many of the most popular publicly-available LLMs will function with Clippy, with one-click installation supported for the newest from Google, Meta, Microsoft, and Qwen. Clippy’s original art and animations are joined by a Windows 98-styled chat and settings window.

On installation, Clippy silently cycles through animations while the program automatically downloads Google’s Gemma3-1B model. Once paired with an LLM, the Clippy-bot approximates the original Clippy’s tone thanks to a lengthy prompt instruction that seeks to disguise the model in use. Users are able to edit or replace this starting prompt to get their most Clippy-esque experience (or to give your Clippy more of his highly-memed snark).

Rieseberg, creator of new Clippy, refers to the program as "a love letter and homage to the late, great Clippy," as well as Microsoft’s 90s visual aesthetic. He calls the app a piece of "software art," or, if you don’t like it, "software satire." There is certainly something to be said about the relationship between an artist and programmer designing a quirky writing aide character, and a chatbot later told to attempt to emulate that work — though the list of people waxing philosophical about Clippy likely doesn’t extend far beyond this author.

We’ve seen Clippy replacements before, but this new-and-improved paperclip doesn’t require access to a paid tier of ChatGPT, nor does it seek to modernize Clippy’s look to match Windows’ newer design sensibilities. This is the Clippy you know and love (?): a more 90’s-looking blend of old and new computing sensibilities.

The Clippy Desktop Assistant is available for download for Windows, Mac, and Linux via the project’s website, with a deeper look behind the curtain available on Github. While it's not likely to revolutionize any desktop workflows, this new Clippy stands at the ready for those happy few with one foot in the vector-graphics of the 90s... and the other in the AI-present.

Software engineer Sven Scharmentke (AKA Svnscha) has shared a project that they say "might change crash dump analysis forever." The new contextual natural language conversational tool contrasts sharply with manually typing arcane commands into a terminal to analyze crash data, which is the established modus operandi of a professional software engineer, even in 2025. Svnscha goes on to assert that the new open-source tool, available as mcp-windbg on GitHub, can go deep. The AI runs a "simply amazing" array of WinDBG/CDB commands, boosting productivity and eradicating a once tedious task.

AI peddlers have developed large language models that initially targeted creative writing and have since moved to visual arts – graduating from hand-drawn style art and photorealistic creations to videos in recent times. AI has also made significant inroads into computer programming. However, most people want AI to leave the fun, creative stuff to humans and take the sting out of dull, soul-crushing, repetitive tasks. This seems to be where Svnscha's mcp-windbg might actually succeed.

In brief, the mcp-windbg tool enables AIs to interact with Microsoft's multipurpose debugger WinDBG. Its cunning skill is in giving LLMs the ability to execute debugger commands. And, even if you have swallowed the WinDBG manual, this AI will likely know debugger commands better than you. Copilot will probably also interpret assembly code, decode hexadecimal, traverse structures with symbols, and perform other tasks much better than you. You just have to ask the right questions.

Background and demos

Svnscha goes into some detail about their prior frustration in debugging crashes, the lightbulb moment to involve AI, and how the mcp-windbg tool works, all in a dedicated blog post. The software engineer humbly admits the 'hard part" of this work was implementing the CDB (Microsoft's Command-Line Console Debugger) interaction layer, and this was done during a session "vibe-coding with two coffees on a Saturday morning."

However, the videos shared help encapsulate the leveraging of Copilot with CDB. There are two to watch on the linked blog, starting with a Crash Analysis and Automated Bugfix using Copilot, followed by an Automated Crash Dump Analysis of Multiple Crash Dump Files.

What you first see shows Svnscha installing mcp-windbg, then running a Windows app that crashes. Next, GitHub Copilot is fired up and asked to find the crash dump and fix the underlying code issue. "The AI quickly identifies that the application crashed, explains which specific conditions led to the crash, and suggests a fix," says Svnscha.

The second video is similar but shows the tool analyzing multiple files simultaneously.

'Simple wrapper' lifts crash analysis from the stone age to the rocket age

Established in 2003, SilverStone Technology has earned a solid reputation for producing high-performance PC hardware and accessories, with a particular focus on power supply units (PSUs), cases, and cooling solutions. Over the years, SilverStone has been recognized for balancing innovation, efficiency, and compact design, catering to a wide range of users, from gamers to professionals.

This review will delve into the latest revision of Hela 850R Platinum, SilverStone's latest series update to meet the ATX 3.1 certification, and determine if it deserves a place in our list of best power supplies. Rated at 850W and certified with the 80Plus Platinum standard, this PSU aims to provide exceptional efficiency and reliability for demanding systems. The first version of the Hela 850R Platinum was one of the first ATX 3.0 units to hit the market, and as we will see in this review, the changes with the new version are much more than just minor changes.

Specifications and Design

In the Box

We received the SilverStone Hela 850R Platinum in robust packaging with a distinctive blue and yellow theme. The PSU itself is encased in a protective nylon pouch and secured by foam inserts to prevent damage during transit.

SilverStone provides a modest bundle that includes just the essentials: mounting screws, an AC power cable, a few cable ties, and three standard cable straps. While the accessory pack is nearly minimal, it covers everything required for installation and basic cable management.

The Hela 850R Platinum features all-black cables that are flat and ribbon-like, without any exterior sleeving. The cabling includes a 12V-2x6 connector and four 6+2 pin PCI Express connectors, ensuring compatibility with modern high-powered components. Given the unit's power output, the number of connectors is more than sufficient.

External Appearance

Aesthetically, SilverStone kept the design simple, opting for a satin black chassis that resists fingerprints and offers a clean, professional look. Measuring 140mm in length, the unit adheres to the ATX design recommendation, ensuring broad compatibility with most cases.

The fan grille is integrated into the chassis, showcasing a unique design pattern that adds a subtle visual touch. The right and left sides of the PSU feature SilverStone's branding stickers, maintaining a minimalist appearance.

At the rear, one will find the AC receptacle, power switch, and a button to toggle hybrid fan mode, which allows the fan to remain off under low loads for silent operation. The modular cable connectors are located at the front, with a subtle legend printed nearby for easy identification.

Internal Design

Cooling is managed by a Globe S1202512HP-4M 120 mm fan with a fluid dynamic bearing (FDB) engine, balancing longevity with quiet operation. SilverStone had to use an 120 mm fan as a 135 mm model does not fit in such a small chassis. It is not the highest quality fan but it is acceptable. The fan remains off at low loads, activating only when necessary, reducing noise and enhancing lifespan.

Enhance Electronics serves as the OEM for the Hela 850R Platinum, introducing a new platform distinct from its predecessor. The internal layout is reminiscent of older designs, with green PCB coloring and simplistic heatsinks, and a quick glance could fool even an expert. A closer look, however, reveals exceptional quality parts and modern topologies.

The filtering stage is well-implemented, consisting of four Y capacitors, two X capacitors, and two inductors to suppress noise and interference. The Active Power Factor Correction (APFC) stage utilizes two Oriental Semi OSG55R140F MOSFETs and a diode, supported by a large inductor and a Nippon Chemi-Con 680 μF capacitor.

On the primary side of the main transformer, two Oriental Semi OSG55R140F MOSFETs form the primary inversion side, operating under a simple half-bridge LLC topology. All primary-side active components are mounted on the same heatsink, which feels undersized given the unit's wattage.

The secondary side employs eight IPS 014N04SA MOSFETs to generate the main 12V rail directly from the transformer. The 3.3V and 5V rails are derived through DC-to-DC converters mounted on a vertical daughterboard. Secondary side capacitors are a mix of Rubycon and Unicon products, both reputable brands known for their reliability. Overall, the Hela 850R Platinum's internal design is straightforward, relying on quality components but lacking advanced cooling measures.

Cold Test Results

Cold Test Results (25°C Ambient)

For the testing of PSUs, we are using high precision electronic loads with a maximum power draw of 2700 Watts, a Rigol DS5042M 40 MHz oscilloscope, an Extech 380803 power analyzer, two high precision UNI-T UT-325 digital thermometers, an Extech HD600 SPL meter, a self-designed hotbox and various other bits and parts.

During cold testing, the SilverStone Hela 850R Platinum does meet the CLEAResult 80Plus Platinum certification requirements with an input voltage of 115 VAC. The PSU does not have a Cybenetics efficiency certification at the time of this review. It achieves an average nominal load efficiency of 91% with an input voltage of 115 VAC, which rises to 92.2% under a 230 VAC supply. The efficiency peaks at around 50% load and remains fairly robust throughout the nominal load range (10–100%), while efficiency at very low loads is acceptable.

The Globe 120 mm fan remains inactive until the load surpasses 300 watts. It operates fairly quietly up to approximately 600 watts before increasing speed in noticeable steps, ensuring effective thermal management without ever reaching maximum speed at full load under room-temperature conditions. The Hela 850R will be noticeable if heavily loaded for prolonged periods of time but noise figures are reasonable and likely far lower than those of the PC’s other cooling systems.

Hot Test Results

Hot Test Results (~45°C Ambient)

Under elevated ambient temperatures, the Hela 850R Platinum exhibits a measurable decline in performance, with the average nominal load efficiency dropping to 89.6% at 115 VAC and 90.7% at 230 VAC. This reduction of about 1.4% in hotter conditions was a bit higher than we typically expect, yet the unit continues to perform within acceptable limits without significant thermal stress.

In these warmer conditions, the fan activates only marginally earlier compared to cold tests but accelerates far more rapidly with increasing load, reaching its top speed when the load is approximately 700 watts. Although the fan becomes noticeably loud under sustained heavy loads, internal temperatures rise significantly, still, these remain within safe operating boundaries and do not trigger the Over Temperature Protection (OTP).

PSU Quality and Bottom Line

Power Supply Quality

The SilverStone Hela 850R Platinum demonstrates commendable electrical performance and power quality. Voltage ripple is well contained, with the 12V rail showing a maximum ripple of 36 mV, and the 5V and 3.3V rails registering a maximum of 22 mV and 20 mV, respectively. Voltage regulation is excellent, at 0.6% for the 12V rail, 1.2% for the 5V rail, and approximately 1% for the 3.3V rail.

During our thorough assessment, we evaluate the essential protection features of every power supply unit we review, including Over Current Protection (OCP), Over Voltage Protection (OVP), Over Power Protection (OPP), and Short Circuit Protection (SCP). All protection mechanisms were activated and functioned correctly during testing.

The OCP is set to activate at 146% for the 3.3V rail, 150% for the 5V rail, and 128% for the 12V rail, with an OPP threshold of 132%. While these thresholds are on the higher side, they are acceptable for an ATX 3.1 compliant unit.

Bottom Line

SilverStone Technology has long been regarded as a reputable manufacturer of PC hardware, known for its emphasis on quality, performance, and compact design. The Hela 850R Platinum represents SilverStone's latest effort to stay at the forefront of PSU design by embracing the ATX 3.1 standard while maintaining the Platinum efficiency rating. Even though most manufacturers made very minor changes (if any at all) to switch their units from ATX 3.0 to ATX 3.1, SilverStone revamped the unit completely, changing even the OEM of the unit itself. The Hela 850R Platinum is a product aimed at demanding users — such as gamers, content creators, and professionals running high-performance systems — promising solid power delivery and broad compatibility with modern components. However, with a retail price of around $200, it enters a highly competitive market segment, where expectations are as high as the price tag.

In terms of performance, the Hela 850R Platinum excels during cold testing, where it easily meets its 80Plus Platinum certification benchmarks with an input voltage of 115 VAC. At an ambient temperature of 25°C, the unit achieves an impressive nominal load efficiency of 91% at 115 VAC, which further improves to 92.2% under a 230 VAC input. These efficiency figures peak at approximately 50% load and remain stable throughout the nominal load range from 10% to 100%. The Globe 120 mm fan operates in a zero-RPM mode until the load exceeds 300 watts, ensuring quiet operation during moderate workloads and efficient thermal management without sacrificing performance.

However, the unit's performance under elevated temperatures presents a more nuanced picture. During hot testing at around 45°C ambient, the Hela 850R Platinum experiences a modest decline in efficiency, with figures dropping to 89.6% at 115 VAC and 90.7% at 230 VAC - a significant reduction of roughly 1.4%. Although this decrease is within acceptable limits, even if only barely, the response of the cooling system under these conditions is notable. The fan, which remains largely inactive during light loads, accelerates more rapidly in warmer conditions and reaches its maximum speed at approximately 700 watts. While this ensures that internal temperatures are kept in check, the resulting noise can be somewhat intrusive under sustained heavy loads. Despite this, the PSU’s electrical performance remains robust, with exemplary power quality delivery.

In conclusion, the SilverStone Hela 850R Platinum stands out as a high-quality power supply unit that delivers exceptional efficiency, strong performance, and reliable protection features. The unit’s compact 140mm form factor, full modularity, and ATX 3.1 compliance make it well-suited for powerful builds. However, at a retail price of approximately $200, the unit faces stiff competition from other PSU offerings in the same category. While its performance merits and premium features justify the cost to some extent, potential buyers must consider whether the investment aligns with their budget and needs in a highly competitive market.

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Microsoft CEO Satya Nadella revealed that the company now uses Artificial Intelligence to write between 20% and 30% of the code powering its software. Satya joined Meta CEO Mark Zuckerberg this Tuesday at the LlamaCon conference to discuss developments in AI and their contributions to the open-source ecosystem. The fact that a major company like Microsoft relies so much on AI underlines how this technology is revolutionizing software development. Still, it also draws attention to the growing unease and uncertainty experienced by fresh software developers.

Almost every company uses Artificial Intelligence to a certain extent. At its Q3 financials last year, Sundar Pichai revealed that 25% of new code at Google is AI-generated. As it stands, AI is mostly there to power repetitive, data-heavy, and predictable tasks, which would yield a noticeable gain to corporate efficiency by cutting down on entry-level jobs. While it is true that AI-generated code has improved significantly over the recent years, it still requires senior developer oversight to ensure the production environment doesn't go haywire.

Mark and Satya spent part of their discussion detailing each company's AI adoption scale. AI generates one-third of the code in Microsoft's repositories and projects. The Meta CEO didn't provide an exact figure, but did outline plans for a future AI model to build and create future iterations of their Llama AI models. Although using AI to design AI might sound counterintuitive, there's an entire dedicated field that focuses on automating this design process, called AutoML.

RTX 5060 Ti 16GB vs RTX 4060 Ti 16GB Introduction

The Nvidia GeForce RTX 5060 Ti 16GB and RTX 4060 Ti 16GB represent the new and old guards of the 16GB realm for Team Green. Both of these are mainstream GPU offerings, ostensibly priced in the $430–$500 range. We weren't particularly impressed with the 4060 Ti 16GB at launch, but our feelings have tempered over time — helped by an unofficial $50 price cut — with both having appeared on our list of the best graphics cards.

The Nvidia Ada Lovelace 4060 Ti 16GB card originally launched over a month after the initial RTX 4060 Ti 8GB card, with a $100 price premium. Throughout the ensuing two years, the 8GB card has been more readily available, though as noted already, street prices on the 16GB card did drop by $50 at times. 

Conversely, the RTX 5060 Ti 16GB and 8GB variants technically launched at the same time on April 16, 2025, with only a $50 price gap. So far, the 16GB models have been far more widespread in the U.S., to the point where we have not yet been able to purchase an 8GB card at retail for testing.

Given the ongoing concerns with having only 8GB of VRAM, we feel it makes the most sense to compare the new and old 16GB xx60 Ti cards. We'll look primarily at performance, as well as power, features, and pricing. In theory, this should be a very easy win for the new Nvidia Blackwell GPU, but let's see how they actually stack up.

RTX 5060 Ti 16GB vs RTX 4060 Ti 16GB Specifications

Specifications for the RTX 5060 Ti 16GB and RTX 4060 Ti 16GB are mostly similar, with a few key upgrades for the newer GPU. While both the GB206 and AD106 dies have a maximum of 36 SMs (Streaming Multiprocessors), the 5060 Ti uses the fully enabled chip while the 4060 Ti disables a pair of SMs. That cascades through the GPU shaders, tensor cores, RT cores, and TMUs, giving the 5060 Ti a theoretical 6% advantage. However, clock speeds also factor into performance, and the 5060 Ti comes with a 2,572 MHz boost clock compared to 2,535 MHz on the 4060 Ti, another 1.5% advantage.

The core counts are a "hard" spec, but the clock speeds are a bit fuzzy. Power limits also come into play, and the 5060 Ti has a 180W TGP compared to 165W on the 4060 Ti 16GB. In practice, across our 21-game test suite, the 5060 Ti 16GB averaged clock speeds of 2,776 MHz compared to 2,725 MHz on the 4060 Ti 16GB — a slightly larger 1.9% advantage in favor of the newer card. Combined with SM and core counts, that gives the 5060 Ti 16GB an 8% advantage in raw compute.

That's pretty negligible in terms of a gen-on-gen increase, but architectural updates can further impact the real-world performance. More pertinent than the raw compute is the memory subsystem. The RTX 5060 Ti 16GB has 28 Gbps GDDR7 memory while the RTX 4060 Ti 16GB has 18 Gbps GDDR6 memory. That means the newer part has a 56% advantage in raw memory bandwidth — though architecture and timings can still play a role.

Taken together, based on the specifications, you should expect to see the newer GPU outperform its predecessor by anywhere from 8% to 56%, depending on whether a particular game or workload relies more on raw compute, memory bandwidth, or a blend of the two. In practice, compute tends to be the more important metric. As such, we'd expect a 15~25 percent lead from the 5060 Ti. Let's see how things go in actual benchmarks.

RTX 5060 Ti 16GB vs RTX 4060 Ti 16GB Gaming Performance

For most people, gaming performance will be the most important factor in choosing a graphics card. We have a test suite of 21 games, 6 of which have ray tracing enabled and 15 with pure rasterization. Upscaling and frame generation are disabled for all these tests, as we view those as "performance enhancers" rather than baseline features that should be compared.

Both the RTX 5060 Ti 16GB and RTX 4060 Ti 16GB support Nvidia's various DLSS features, though the 5060 Ti also offers MFG (Multi Frame Generation) — which can smooth out performance but definitely doesn't improve the actual feel of games that support it as much as the numbers might suggest. The experience of DLSS 3 framegen in our view tends to be inflated by about 50–60 percent (meaning, divide the resulting number by 1.5–1.6 to get a realistic view of what it feels like), while MFG 4X is about a 170–200 percent inflation (divide by 2.7–3.0).

Those are fuzzy approximations, but basically if you get less than a 50% "increase" in framerates via framegen, we would argue that it actually feels like a step backward. Likewise, less than a 170% increase from MFG 4X would be a net loss. Or to put hard numbers on it, if the base performance is 60 FPS, framegen needs to run at 90 FPS or more and MFG 4X needs to run at 160 FPS or more. Some would argue even those figures are too generous, but our main point is that "FPS" inflation via frame generation techniques has been heavily abused as a true indication of performance.

The charts and tables provide all the important numbers, so we're not going to dig into every single result. We're mostly interested in the overall trends, recognizing that individual games can come in above or below the averages. That's why we have an extensive 21 game test suite, with equal weighting given to each result via a geometric mean calculation.

The nice thing about comparing two Nvidia GPUs, even though they're different architectures, is that relative performance ends up being pretty consistent across both rasterization and ray tracing games. AMD and Intel GPUs vary quite a bit more, depending on what specific games are used.

Overall, the 5060 Ti 16GB outperforms the RTX 4060 Ti 16GB by 16% at 1080p medium, 17% in the ray tracing suite, and 16% in the rasterization suite. That means the 5060 Ti and 4060 Ti generally aren't CPU limited, even at 1080p medium. The minimum FPS (1% lows) shows similar consistency. Looking at the individual games, the 5060 Ti 16GB leads by 6% (Assassin's Creed Mirage) to as much as 29% (A Plague Tale: Requiem).

Bumping up to 1080p ultra settings, most of the deltas are virtually unchanged. The 5060 Ti 16GB still leads by 16% overall, 17% in the ray tracing suite, and 16% in the rasterization games. Minimum FPS ends up slightly closer overall at 18%, but with a wider 23% spread in our ray tracing tests and 16% in the rasterization games. Across all 21 games, we measured an 8% (Starfield) to 26% (Plague Tale: Requiem and Space Marine 2) delta in favor of the 5060 Ti.

Given the memory bandwidth advantage, the 5060 Ti 16GB should increase its overall lead at higher resolutions, and it does — just not by much. It's 18% faster overall at 1440p ultra, and 18% in both the rasterization and ray tracing suites. The various games show an advantage of 9% (Starfield) to 29% (Plague Tale and Space Marine 2, again).

Finally, at 4K ultra, we see the biggest lead for the 5060 Ti 16GB. Across the full 21-game suite it's 21% faster than the 4060 Ti 16GB, with a 20% lead in the ray tracing games and a 22% lead in the rasterization tests. The smallest advantage improves to 12% (Avatar), with the biggest lead being 33% (Space Marine 2).

RTX 5060 Ti 16GB vs RTX 4060 Ti 16GB AI and Content Creation Performance

Looking at other potential workloads, we have various disciplines that all loosely fall under the content creation umbrella. AI workloads include Stable Diffusion image generation, LLaMa text generation, image classification, inference, and other tasks. We also have 3D rendering via Blender, video transcoding using SPEC Workstation 4.0, and the Viewport test suite from SPEC Workstation 4.0.

Many people — particularly gamers — may never do any of these tasks on their PC, though it's worth nothing that Nvidia's push for "neural rendering" techniques can also extend into AI-powered NPCs. That's not something we can directly benchmark at present, but there's potential for the AI performance to become more important in the coming years if AI NPCs catch on in some major games.

Of all the content creation tasks, we think the AI tests deserve the most attention. As noted above, such workloads could become a bigger part of how people use their PCs and graphics cards in the future, even within games. Overall, the 5060 Ti 16GB delivers about a 24% lead in AI workloads compared to the RTX 4060 Ti 16GB. It's also worth mentioning that none of these AI tests at present leverage the FP4 support that was added to the tensor cores in the Blackwell architecture, so there's potential for even more of an AI deficit if future applications make use of that number format.

Video transcoding ends up being a virtual tie, indicating there's not a huge difference between the NVENC/NVDEC blocks in Ada and Blackwell. The 5060 Ti takes a 4% lead by virtue of running at slightly higher clock speeds, though Nvidia did supposedly improve the encoding quality — that's not something these tests measure, unfortunately.

3D modeling using Blender basically matches what we saw in gaming performance. Overall, the 5060 Ti 16GB runs 20% faster than the 4060 Ti 16GB, with a spread of 14% to 29% in the three different scenes. SolidWorks agrees with that number, while 3ds Max shows an even larger 31% lead for the 5060 Ti.

Those last two are part of the SPEC Workstation 4.0 Viewport tests, which show a 17% lead overall for the 5060 Ti, with a range of 1% (Creo) to as much as 31% (3ds Max). Most of the SPEC Workstation tests are truly for professionals, however, so we wouldn't put too much weight on them. Most paid professionals will have a workstation equipped with a workstation GPU (e.g. Nvidia RTX 6000 Ada Generation or the new RTX Pro 6000 Blackwell).

RTX 5060 Ti 16GB vs RTX 4060 Ti 16GB Features and Software

The vast majority of Nvidia features and software tools are available on both the RTX 4060 Ti 16GB and the RTX 5060 Ti 16GB. As such, it's easy to think that the two are "tied" in features. However, there are a few noteworthy exceptions.

First, the Blackwell RTX architecture adds support for Multi Frame Generation. We don't love the way the numbers are used to indicate massive performance improvements (i.e. Nvidia CEO Jensen Huang tried to claim that an "RTX 5070 is as far as an RTX 4090" at the launch event), but MFG isn't inherently bad. There are games where it works better, and the additional frame smoothing can make games feel better, subjectively. It's a nuanced discussion, in other words, and not the "fake frames BAD" mentality that often gets tossed around.

Blackwell RTX, and by extension the RTX 5060 Ti 16GB, is also supposed to have better support for so-called neural rendering (i.e. Microsoft's Cooperative Vectors API). We don't yet know precisely how much better it might be compared to older GPUs, but we do know Cooperative Vectors is supposed to work with all RTX GPUs — and that it's supposed to be better on Blackwell RTX.

As noted earlier, the RTX 5060 Ti also has support for FP4 (and FP6) numerical formats in its tensor cores. Making proper use of FP4 presents challenges for AI models, but various algorithms have demonstrated superior performance and reduced memory requirements with a "negligible" drop in overall inference quality. As AI continues to evolve, it's reasonable to expect the use of FP4 to become more commonplace.

We've seen that with FP8 support as well. First introduced with the RTX 40-series GPUs, the use of FP8 modes was relatively rare for most of the past 2.5 years. However, Nvidia's new DLSS Transformers algorithms for upscaling (super resolution) and ray reconstruction appear to leverage native FP8 support. It's why the performance hit for DLSS Transformers tends to be substantially lower on RTX 40- and 50-series GPUs compared to RTX 30- and 20-series GPUs.

But Nvidia giveth and Nvidia also taketh away. Blackwell graphics cards no longer have PhysX support, so the RTX 5060 Ti 16GB simply can't run PhysX in older games that used the feature. (Not many recent games have used GPU PhysX, so this mostly applies to games like the Batman: Arkham series and Mirror's Edge, all of which are at least 10 years old now.) 32-bit CUDA support was likewise retired, which again mostly impacts older apps that are no longer being actively updated.

RTX 5060 Ti 16GB vs RTX 4060 Ti 16GB Pricing

The RTX 4060 Ti 16GB launched in July 2023 with an MSRP of $499, $100 more than the 8GB variant. The RTX 5060 Ti 16GB came out in April 2025 with a $429 MSRP. On paper, that clearly suggests a pricing advantage for the 5060 Ti 16GB. In practice, it's also an advantage when shopping around for new cards, but that's because the 4060 Ti 16GB (and the rest of the RTX 40-series) has been retired.

Since both GPU lines are made using TSMC's 4N process node, there's no reason for Nvidia to continue producing the older chips, and in fact all indications are that wafer orders for Ada Lovelace may have ended in the fall of 2024. Supplies have completely dried up and prices have shot up as a result.

The availability of RTX 50-series GPUs has been relatively poor compared to what we saw with the 40-series launch. Sure, the RTX 4090 sold out for the first few months, but eventually it reached the point where you could expect to find some models selling slightly below the base MSRP, and all of the other 40-series GPUs eventually dropped below the base MSRP by late 2023 and early 2024. So far, the RTX 50-series has rarely even approached MSRP, often selling at 20% to 50% above the minimum suggested price.

At present, the least expensive RTX 5060 Ti 16GB card we can find is an Asus Dual RTX 5060 Ti 16GB for $492.98 at Newegg. That's about 15% above the base MSRP that Nvidia set, but Nvidia can't force its add-in board (AIB) partners to actually sell cards at MSRP. With ongoing GPU shortages, it's little surprise that nearly all 50-series GPUs are "overpriced" relative to MSRP.

As bad as that might seem, the RTX 4060 Ti 16GB is in an even worse state. With production of those GPUs likely halted last year, only limited supplies remain. Mostly, it's third-party marketplace resellers on Newegg, Amazon, and similar storefronts trying to make some serious money. Even the 8GB cards start at $575 (MSRP is supposed to be $400), while the cheapest RTX 4060 Ti 16GB costs $679. Yeah, that's a bad choice unless for some reason you specifically need the older hardware.

RTX 5060 Ti 16GB vs RTX 4060 Ti 16GB Final Verdict

This was pretty much a foregone conclusion, but the RTX 5060 Ti 16GB easily beats the RTX 4060 Ti 16GB in every conceivable metric. There's only one potential advantage to the 4060 Ti, and it's nebulous at best: It supports PhysX and 32-bit CUDA (though both are likely deprecated and will fade away in the coming months).

In contrast, the 5060 Ti 16GB offers about 20% higher gaming performance on average, across nearly all tested resolutions and settings. It's also about 20% faster in AI workloads and 3D rendering, and about 17% faster in a selections of professional applications. That's largely thanks to having 56% more memory bandwidth, courtesy of GDDR7, though other architectural upgrades and slightly increased core counts also factor into things.

The real kicker is the price. Unless you're lucky enough to find an RTX 4060 Ti 16GB on a steep discount, the 5060 Ti 16GB costs quite a bit less at popular online retail sites like Newegg and Amazon. Just say no, in other words. Slower performance, missing new features, and a price that's currently 38% higher? Pass. It's also a poor proposition when you look at total system cost (using $750 for the price of the rest of the PC). The 5060 Ti 16GB delivers 24% more performance per dollar than the RTX 4060 Ti 16GB in that case (using MSRPs for both, which benefits the 4060 Ti).

If you're wondering about power use and efficiency, even though the TGP (Total Graphics Power) on the 5060 Ti 16GB is 15W higher on paper, in practice it averaged lower power use in our testing. Power use was 1W higher at 4K ultra (a setting both GPUs struggle with), 5W lower at 1440p ultra, and 8–9 watts lower at 1080p. With 21% higher performance at 4K, that works out to much improved overall efficiency.

Hopefully it goes without saying, but just because the RTX 5060 Ti 16GB offers more performance and costs less than a new 4060 Ti 16GB, that doesn't mean people who already own the prior generation card should be in a rush to upgrade. A 20% performance uplift combined with a few new features might be nice to have, but we generally only recommend upgrading if you can get at least a 50% performance improvement, and doubling performance is often possible if you're upgrading from a card that's a couple of generations old.

Let's also not forget that there are 8GB variants of both GPUs. Again, just say no to such cards. Unless you can score a particularly impressive deal, the lack of VRAM is increasingly becoming a problem for 8GB cards. Sure, older and lighter games can run just fine on such GPUs, but it's the newer games that need faster hardware, and they're having problems. The 4060 Ti 16GB and 8GB offer nearly identical performance in our test suite at 1080p, but the 16GB card pulls ahead by 7% at 1440p, and by 36% at 4K — with the most demanding newer games exceeding those averages. We anticipate the story will be largely the same with the 5060 Ti 8GB.

So there you have it: While the generational gains of the RTX 5060 Ti 16GB over the RTX 4060 Ti 16GB aren't as large as we've seen with prior architectural upgrades, the lack of progress on process nodes and the relatively similar die sizes and transistor counts mean we shouldn't expect 50% performance improvements at the same price going forward. 20% more performance for an ostensibly 14% lower price is about as good as we're likely to see. That's about 40% more performance per dollar, which beats any of the other RTX 50-series GPUs.

RTX 5070 vs RTX 5060 Ti Introduction

The Nvidia GeForce RTX 5070 and RTX 5060 Ti 16GB are the two most recent GPUs launched by Team Green. Since the beginning of 2025, Nvidia has released five Blackwell RTX 50-series GPUs — six if you want to count the RTX 5060 Ti 8GB as a separate entry.

To say that supplies have been limited and insufficient to keep up with demand would be a gross understatement, but the same applies to any of the best graphics cards right now. In fact, of all the Blackwell RTX GPUs, the 5070 and 5060 Ti are currently the most readily available; just don't expect to find most models at MSRP.

Our GPU benchmarks hierarchy ranks all the graphics cards by performance, and naturally the more expensive cards come out ahead of their less expensive siblings. But how do the RTX 5070 and RTX 5060 Ti 16GB stack up? We'll look at the performance, approximate pricing — because no GPU prices are reliably set in stone right now — as well as other aspects of the cards to help you decide which one might be right for your gaming PC.

Most of the features between the two GPUs will be identical. Both use the Blackwell architecture and support DLSS 4 and MFG, for example. Still, let's start with the specifications to see how the 5070 and 5060 Ti stack up.

RTX 5070 vs RTX 5060 Ti Specifications

As you'd expect, the RTX 5070 offers more raw performance on paper and in practice. It uses a GPU that's 45% larger, with 41.6% more transistors. More importantly perhaps, it has 33% more SMs, tensor cores, RT cores, and related computational units. It also has 50% more memory bandwidth and a 50% wider memory interface, plus 50% more L2 cache.

Memory interfaces don't scale as well as core logic on modern process nodes, and there are other aspects of the Blackwell GPU — the video encoders/decoders, display outputs, etc. — that don't really change, which explains why the chip size and transistor counts don't necessarily scale linearly.

Clock speeds aren't quite the same, at least on paper. The 5060 Ti has a 2572 MHz boost clock and the 5070 has a slightly lower 2512 MHz boost clock. But we're also looking at the 5070 Founders Edition with reference clocks, and a PNY 5060 Ti 16GB OC that has a 2692 MHz boost clock. Plugging in those numbers gives the 5070 30.9 teraflops of FP32 compute and the PNY 5060 Ti has 24.8 TFLOPS. That means the 5070 'only' has 25% more compute on paper — which isn't actually all that accurate.

In our full suite of gaming benchmarks, the 5070 Founders Edition averaged clock speeds of 2826 MHz while the PNY 5060 Ti 16GB OC averaged 2776 MHz. That means despite theoretically 180 MHz lower clocks, in practice Nvidia's 5070 card came out 50 MHz ahead.

That means the 5070 FE offers about 34.7 TFLOPS compared to 25.6 TFLOPS, a 35.5% advantage. Practically speaking, though, the 5070 should be up to 35% faster in games that are compute bound, and up to 50% faster for games that are memory bandwidth bound — and potentially the 5060 Ti could close the gap if VRAM capacity comes into play.

RTX 5070 vs RTX 5060 Ti Gaming Performance

There are multiple facets of performance to discuss. We'll start with gaming performance, as that's what the majority of people looking at the RTX 5070 and RTX 5060 Ti 16GB care about the most. We have an expanded test suite of 21 games, with 15 rasterization benchmarks and six ray tracing benchmarks — slightly more than the 18 games we use in our GPU hierarchy (we're planning to add these into the hierarchy but haven't tested every GPU yet).

The charts group things together by overall performance (geometric mean across all 21 games, where each gets equal weighting), rasterization-only performance, and ray tracing-only performance. We also have separate tables that show the percentage differences below the charts (sorted in reverse order due to the vagaries of Excel).

It's safe to say that neither the RTX 5070 nor the RTX 5060 Ti 16GB are primarily targeted at native 4K ultra gaming. The 1440p result works as a proxy for 4K with DLSS quality mode upscaling, and the 1080p result stands in for 4K with performance mode upscaling, though there would be potential differences. Upscaling does have some overhead, but since we're looking at two Nvidia GPUs we can expect a pretty consistent delta cause by DLSS. CPU bottlenecks would potentially affect performance at lower resolutions as well, which we can see by the results.

At 4K, the RTX 5070 leads the RTX 5060 Ti 16GB by 32% on average, with a performance difference of -5% (Indiana Jones and the Great Circle) to +43% (A Plague Tale: Requiem). Indiana Jones is known to want a lot of VRAM — you can even run the ultra setting on cards with less than 12GB — while various other games appear to want more memory bandwidth.

Dropping to 1440p, the 5070 lead shrinks fractionally to 31% overall, and this time there are no performance deficits. There may be a few other games out there that can push beyond 12GB of VRAM use, but from our test suite — a rather demanding suite overall, we'd say — 12GB remains sufficient in all games for 1440p, and in all but one of the games for 4K.

1080p starts to run into CPU limits, even more so at medium settings, which shrinks the delta between the 5070 and 5060 Ti to 28% at ultra settings and 24% at medium settings. That's still a sizeable gap, with a few of the games showing basically equivalent performance due to the CPU bottleneck (Baldur's Gate 3, Flight Simulator 2020, Spider-Man 2, Spider-Man: Miles Morales, and Stalker 2 end up tied in one or both cases).

Looking at the rasterization and ray tracing performance, overall there's not much difference at the various resolutions and settings. 4K ultra does run into VRAM limits on one of the RT games, so that the gap is slightly narrower there than at 1440p and 1080p, and our RT test suite does end up with lower FPS than the rasterization games at ultra settings. Medium settings with RT enabled actually runs faster on average for our test games, but only Control and Cyberpunk are truly demanding RT games in our title selection.

RTX 5070 vs RTX 5060 Ti Content Creation Performance

Our content creation test suite consists of a variety of test that all loosely fall into the "professional" category. We have multiple AI tests, 3D rendering benchmarks, video transcoding performance courtesy of SPEC Workstation 4.0, and the SPEC Workstation 4.0 Viewport benchmarks (basically the same tests as SPECviewperf).

As expected, the RTX 5070 generally walks away with the content creation crown. It's 45% faster in the Blender 3D rendering benchmarks, 33% faster in MLPerf text generation tokens per second, and 20–43 percent faster in the Procyon and SPEC AI tests that we ran. The 5060 Ti 16GB did have a faster time to first token in MLPerf, though it's possible that's due to updates to the benchmark (we'll recheck the 5070 in the near future, once our test rig isn't busy).

For SPEC Workstation 4.0's Viewport tests, there's another instance of the 5060 Ti 16GB garnering a win — it's 2% faster in Creo. But overall the 5070 still has a 23% lead. Handbrake video transcoding meanwhile ends up as a tie, which is expected as the fixed function encoding and decoding hardware is the same, so the only difference would be GPU clocks during that test.

Neither GPU is specifically marketed as a professional solution, though we anticipate there will be professional variants of both chips (with more VRAM and drivers that enable additional accelerations in some of the SPEC Viewport tests). Mostly, the AI results are interesting as a hobbyist solution, though it's possible future games might leverage the AI hardware more.

RTX 5070 vs RTX 5060 Ti Conclusion

Which is better: RTX 5070 or RTX 5060 Ti 16GB? Price, as we said at the start, will arguably be the biggest factor in deciding between the RTX 5070 and RTX 5060 Ti 16GB. It's also the one thing that we can't really pin down at present. Since the 5060 Ti launched just a few days ago, pricing and availability are more likely to fluctuate in the near term. The 5070 has been out for a month now, and we have a better idea of what to expect.

First, let's talk about MSRPs and performance. On paper, Nvidia gives the 5060 Ti 16GB a $429.99 MSRP and the 5070 has a $549.99 MSRP — so $430 and $550. That means the 5070 is supposed to cost 28% more than the 5060 Ti 16GB. And based on the performance results, that's exactly in line with what you get.

Linear performance scaling generally means you're better off buying the more expensive card — we would normally expect to see diminishing returns. So if you buy an RTX 5060 Ti 16GB at MSRP and get 0.171 FPS/$ across our gaming tests, versus the RTX 5070 with 0.170 FPS/$, we give the win to the 5070. The reason is because the GPU doesn't exist in a vacuum; there's the rest of the PC to consider.

Alternatively, let's talk current street prices. These will change dramatically over time, but we can currently find an RTX 5060 Ti 16GB for $509.98, or an RTX 5070 for $619.98. That's the second pair of tables in the above gallery, which obviously skews in favor of the 5070 at current prices. And that's what you need to look at, first and foremost.

Finally, imagine a PC where the CPU, motherboard, SSD, RAM, PSU, and case together cost $750 for example. Now you're looking at a total cost of $1,180 for the 5060 Ti 16GB compared to $1,300 for the 5070 build (using the base MSRPs for the GPUs). That works out to just 10% more total money for about the same 25~30 percent performance uplift. It's what the last set of tables above show, and again, it favors the 5070 heavily.

Given the 25~30 percent performance advantage of the RTX 5070 over the RTX 5060 Ti 16GB, spending up to 30% more on the RTX 5070 would make sense. It's the clear winner at current online prices, or at the given MSRPs. In fact, we would argue that it's worth spending up to 40% more for the 5070 compared to the 5060 Ti, as we normally expect diminishing returns.

We don't know where prices will end up, in the U.S. or elsewhere, but you can use the above 30–40 percent figure as a guideline. If the RTX 5070 were to cost $550, we wouldn't recommend spending more than $425 on the RTX 5060 Ti 16GB — and $400 would be better. Conversely, if the best price you can find on the RTX 5070 ends up being $700, then the 5060 Ti 16GB would be worth $500 to at most $540. (And you can replace the dollar signs with whatever monetary symbol you choose.)

Long-term, we expect both GPUs will trend toward about a 30% higher price for the RTX 5070 vs RTX 5060 Ti 16GB. Anything less makes the slower card very difficult to justify, even if it does technically offer more VRAM. So unless you specifically need a card with 16GB, perhaps to run a particular LLM, the RTX 5070 offers the better overall value right now, and will probably continue to do so in the future.

With the release of its latest models earlier in the week, OpenAI seems to have inadvertently tuned ChatGPT to become a potent geo-guesser. The newly available o3 and o4-mini are so good at this ‘reverse location search’ task that showing off this newfound functionality has become a viral social media trend, notes TechCrunch. However, this apparent geographic needle-in-a-haystack hunting improvement raises privacy concerns. And pro geo-guessers on social media platforms might be a little worried too.

This newfound ability of ChatGPT is a great example of the strengthened visual reasoning being brought to the platform with model updates. It can now reason based on the content of uploaded images and perform some Photoshop-esque tasks like cropping, rotating, and zooming in.

As per the source report, there are plenty of examples of users of this famous AI chatbot now using it to drill down on the location of various images. A popular jape is to ask ChatGPT to imagine it is playing the online GeoGuessr game and provide the answer based on supplied imagery. Below, we’ve embedded an example of ChatGPT's location divining skills, shared by AI enthusiast YouTuber and Twitterer Brendan Jowett.

As Jowett points out, the newly popular ChatGPT ‘reverse image search’ functionality has privacy implications, and raises particular concerns with regard to doxing. Doxing is publicly sharing someone’s private information, particularly location / residence, on the broad internet. People are commonly doxed with malicious intent, with the perpetrator hoping to direct loonies and cranks to visit upon the victim(s).

Interestingly, TechCrunch notes that ‘Geoguessr’ ability isn’t new for ChatGPT with the release of o3 and o4-mini. It is just the trend / awareness that has ballooned. It is said that o3 is particularly good at reverse location search, but GPT-4o, a model released without image-reasoning, can sometimes outpace o3, and deliver the same correct answer “more often than not,” says TechCrunch.

Before we go, it is worth mentioning that AI geo-guessing isn’t a totally dependable or 100% accurate function of ChatGPT with the latest models. That’s not surprising. Also, TechCrunch got a statement from OpenAI on its viral GeoGuessr success. In brief, the artificial intelligence pioneer said that, while it works to improve its tools with things like visual reasoning, it also spends time training models to refuse requests for private or sensitive information. Creative users may be able to sidestep safeguards for a time, but OpenAI indicated it will take action where it sees evidence of abuse of its usage policies.

AMD is reportedly preparing to debut its RDNA 4 workstation GPU offerings for desktops, presumably under the Radeon Pro W9000 family. As put forwaard by Hoang Anh Phu, who frequently obtains inside scoops, AMD is considering using the Navi 48 XTW die for its top-end SKUs, paired with 32GB of video memory, likely GDDR6. As always, this leak shouldn't be taken as definitive, but there's likely some truth to it given the proximity of Computex next month, followed by AMD's Advancing AI event in June.

Radeon PRO GPUs are aimed at workstation setups, rivaling Nvidia's (former) Quadro or (now incumbent) RTX PRO offerings for prosumers. These graphics cards bridge the gap between consumers and server domains, for applications like AI, HPC, DCC, CGI, CAD, VR/AR, and the list goes on.

It seems that AMD is sticking to more conservative figures for its flagship workstation offerings this generation. That's somewhat expected since Navi 48 (356mm²) is in the same ballpark as GB203 (378mm²), found in the RTX PRO 4500 Blackwell. Nvidia's top-end GB202 at 750mm², is home to the RTX PRO 6000 Blackwell featuring a massive 96GB frame buffer.

Navi 48, with its 256-bit interface, enables either 16GB of memory (via eight 32-bit channels) or a theoretical maximum of 32GB in clamshell mode, which is the exact configuration being reported here.

Lvmin Zhang at GitHub, in collaboration with Maneesh Agrawala at Stanford University, has introduced FramePack this week. FramePack offers a practical implementation of video diffusion using fixed-length temporal context for more efficient processing, enabling longer and higher-quality videos. A 13-billion parameter model built using the FramePack architecture can generate a 60-second clip with just 6GB of video memory.

FramePack is a neural network architecture that uses multi-stage optimization techniques to enable local AI video generation. At the time of writing, the FramePack GUI is said to run a custom Hunyuan-based model under the hood, though the research paper mentions that existing pre-trained models can be fine-tuned using FramePack.

Typical diffusion models process data from previously generated noisy frames to predict the next, slightly less noisy frame. The number of input frames considered for each prediction is called the temporal context length, which grows with the video size. Standard video diffusion models demand a large VRAM pool, with 12GB being a common starting point. Sure, you can get away with less memory, but that comes at the cost of shorter clips, lower quality, and longer processing times.

Enter FramePack: a new architecture that compresses input frames, based on their importance, into a fixed-size context length, drastically reducing GPU memory overhead. All frames must be compressed to converge at a desired upper bound for the context length. The authors describe the computational costs as similar to image diffusion.

Microsoft researchers just created BitNet b1.58 2B4T, an open-source 1-bit large language model (LLM) with two billion parameters trained on four trillion tokens. But what makes this AI model unique is that it’s lightweight enough to work efficiently on a CPU, with TechCrunch saying an Apple M2 chip can run it. The model is also readily available on Hugging Face, allowing anyone to experiment with it.

Bitnets use 1-bit weights with only three possible values: -1, 0, and +1 — technically it's a "1.58-bit model" due to the support for three values. This saves a lot of memory compared to mainstream AI models with 32-bit or 16-bit floating-point formats, allowing them to operate much more efficiently and require less memory and computational power. Bitnet’s simplicity has one drawback, though — it’s less accurate compared to larger AI models. However, BitNet b1.58 2B4T makes up for this with its massive training data, which is estimated to be more than 33 million books.

The team behind this lightweight model compared it against leading mainstream models, including Meta’s LLaMa 3.2 1B, Google’s Gemma 3 1B, and Alibaba’s Qwen 2.5 1.5B. BitNet b1.58 2B4T scored relatively well against these models in most tests, and even took top honors in a few benchmarks. More importantly, it only consumed 400MB in non-embedded memory — less than 30% of what the next smallest model (Gemma 3 1B) used, which is 1.4 GB.

GPU Benchmarks Introduction

Tom's Hardware exhaustively benchmarks every GPU to find out which are worthy of our list of the best graphics cards. Our GPU benchmarks hierarchy ranks current and previous generation graphics cards by performance. Whether it's playing games, running artificial intelligence workloads, or doing professional video editing, your graphics card typically plays the biggest role in determining performance — even the best CPUs for gaming take a secondary role.

Our 2026 GPU Hierarchy testing spans three generations of Nvidia and AMD graphics cards, as well as Intel's Arc B-series GPUs.

Our testing has been made easier by the fact that no truly new gaming GPUs have been introduced in almost a year. If you haven't already upgraded your graphics card after the GeForce RTX 50-series and Radeon RX 9000-series launches in 2025, well, you're still looking at the exact same products now.

AMD did make its formerly China-only Radeon RX 9070 GRE available globally after Computex 2026, but in our review, we found that $549 product to be too expensive given the level of performance it delivers and the compromises made to hit its price point. Check out that coverage for all the details.

Most of the products we recommend remain at elevated prices compared to their MSRPs, but this is just life in mid-2026. It's admittedly cold comfort, but unless you're shopping for an RTX 5090, graphics card prices haven't risen much more than they already did earlier this year.

Compared to the doubling or tripling of prices we've seen for RAM kits and SSDs in 2026 versus last year, a GPU upgrade remains a relatively affordable (and self-contained) option, either as a boost for an existing PC or part of an all-new parts list.

Let's dive into our ranking of GPUs past and present so you can figure out how all those cards stack up.

Our full GPU hierarchy using traditional rendering (aka rasterization) comes first, and below that we have our separate ray tracing hierarchy. We've also mashed up these results into one overarching ranking for the complete pictures.

The results are all collected at native resolution, without enabling DLSS, FSR, or XeSS upscaling or frame generation.

As a brief refresher of the cards in this version of our hierarchy, Nvidia's cutting-edge Blackwell architecture and its DLSS 4 suite of tech upgrades the quality of both upscaling and ray reconstruction on RTX 50-series GPUs. It also adds Multi Frame Generation support. As of mid-2026, MFG can add anywhere from one to five AI-generated frames in between natively rendered ones.

Even if you're not into framegen, DLSS 4.5 upscaling and its transformer neural network architecture can offer a big boost in image quality at the same output resolution compared to earlier DLSS versions. That tech can benefit all GeForce RTX GPUs going back to the RTX 20-series, albeit at a heavier performance cost on older hardware compared to earlier DLSS versions.

Nvidia's Ada Lovelace architecture powers its prior-gen RTX 40-series cards. Ada introduced DLSS Frame Generation, which can double output frame rates in supported games. Ada cards don't benefit from MFG, though.

AMD's Radeon RX 9000 series cards, powered by its latest RDNA 4 architecture, get a big boost to both ray-tracing and AI capabilities with dedicated RT and matrix math accelerators. AMD uses those capabilities to enable its FSR 4 upscaler and its much-improved image quality in a small but growing range of titles, whether through native support or with driver-level overrides.

The FSR Redstone update last year brought ML Frame Generation to RX 9000-series cards. Like FSR 4.x upscaling, ML Frame Generation can be directly integrated in games or enabled through a driver override.

Meanwhile, the last-gen RDNA 3 architecture powers seven RX 7000-series seven desktop cards. Until very recently, RX 7000-series cards couldn't officially run FSR 4 upscaling, but an official version of that tech will be made available for those cards in July 2026.

Intel's Battlemage-powered Arc B580 and Arc B570 offer major improvements in performance and efficiency compared to its Alchemist architecture. Battlemage only serves the entry-level end of the graphics card market today. Intel introduced a larger Battlemage chip in the form of the Arc Pro B70 earlier this year, but that product is intended for AI and professional visualization, and it's priced like it.

On page two, you'll find our 2025-2026 test data for posterity. Page three has our 2024–2022 benchmark suite, which covers previous-generation GPUs running an older test suite and a Core i9-12900K. Page four has an even older 2020–2021 test suite with only raster games, running on a Core i9-9900K testbed. The legacy tables are no longer being actively updated. There's also the "Ancient Legacy GPU hierarchy" (without benchmarks, sorted by theoretical performance) for reference purposes.

The following tables sort everything solely by our performance-based GPU benchmarks, from fastest to slowest. Factors including real-world pricing, graphics card power consumption, overall efficiency, and features aren't factored into the rankings here. The latest results use an AMD Ryzen 7 9800X3D testbed. Here are the tables and benchmark results — rasterization games first, then ray tracing games, and finally the content creation results.

GPU Benchmarks Hierarchy 2026: The tests

For our latest GPU benchmarks, we test every card at a mix of high and ultra settings, depending on the game. We test across three resolutions: 1080p, 1440p, and 4K.

All the scores are expressed as percentages relative to the top-ranking 1080p ultra card, which is of course the GeForce RTX 5090.

Our 2026 test suite comprises the following games. 11 are raster titles, and of those, four have RT support. We test another four games that either require RT to run (DOOM: The Dark Ages and Indiana Jones and the Great Circle) or look best with RT (Assassin's Creed Shadows and Grand Theft Auto V Enhanced.)

• Black Myth Wukong (+RT)
• Alan Wake II (+RT)
• Apex Legends
• Counter-Strike 2
• Fortnite
• Arc Raiders
• Stalker 2
• DOOM: The Dark Ages (RT required)
• Grand Theft Auto V Enhanced (RT only)
• Marvel's Spider-Man 2 (+RT)
• Indiana Jones and the Great Circle (RT required)
• Marvel Rivals
• Assassin's Creed Shadows (RT only)
• Cyberpunk 2077 (+RT)
• Clair Obscur: Expedition 33

GPU Benchmarks Hierarchy 2026: Raster gaming

The FPS score is the geometric mean (equal weighting) of all 11 games. Note that the specifications column links directly to our original review for the various GPUs.

Rasterization GPU Benchmarks, Key Findings

• Unsurprisingly, the RTX 5090 takes the top spot across the board, but prices remain stratospheric in mid-2026. It's also difficult to fully engage the GB202 GPU for pure raster gaming unless you're playing at 4K.
• Among current cards, the RX 9060 XT 8GB and RTX 5060 duke it out for the best performance per dollar at 1080p.
• The Radeon RX 9070 is the 1440p raster value champ. The recently introduced RX 9070 GRE sits just behind, and the RTX 5070 is a somewhat distant third.
• At 4K, the RX 9070 XT is the cheapest way to get into 4K 60 FPS native gaming. But the RX 9070 and RTX 5070 are also strong options among current GPUs if you're willing to enable a dash of upscaling.

Overall, if you're only interested in native raster gaming, Radeons are a strong choice in 2026. But Nvidia offers superior DLSS 4.5 upscaling and Multi Frame Generation support across all RTX 50-series cards, which makes matching your GPU's performance to your monitor's refresh rate a snap.

FSR 4.x upscaling isn't on par with DLSS 4.5 yet, and FSR ML Frame Generation is limited to a 2x framerate boost where it's available.

Rasterization GPU Benchmarks Rankings 2026

Remember that we're not including any upscaling or framegen results in the above table. DLSS, FSR, and XeSS offer differing image quality, and we want to keep things directly comparable.

Don't buy any of the cards at the top of our hierarchy without a high-refresh-rate 2560x1440 or 4K monitor to match. Even one of our best gaming CPUs, like the Ryzen 7 9800X3D, can only do so much when a powerful card becomes CPU-bound.

In general, if you're still running a 60 Hz fixed-refresh-rate monitor, it's entirely possible that you're not seeing all the frames your graphics card can generate. Practically any current-gen graphics card from the RTX 5060 on up is good enough for high-refresh-rate gaming at 1080p in 2026 even without upscaling or framegen, and the RTX 5070 or RX 9070 will give you the same experience at 1440p.

In this era of upscaling and framegen, a high-resolution monitor is no longer an obstacle to the best gaming experience. Upscalers work best with higher-than-1080p output resolutions, and the advent of the DLSS 4.5 upscaling model especially means that you can get both smooth performance and crisp image quality from surprisingly modest graphics cards.

But if you don't have a high-refresh-rate 1440p or 4K monitor to begin with, you can't take full advantage of the free boost to both performance and image quality that AI-powered upscaling offers, nor can you enjoy the full smoothness boost of framegen. Check out our list of the best gaming monitors for a potential upgrade if you're still rocking a 1080p 60 Hz display from the 2010s.

On to our 2026 raster results. Among currently available graphics cards, Blackwell rules the top three spots. The RX 9070 XT ends up fourth, just behind the potent (but much more expensive, as of June 2026) RTX 5070 Ti.

Moving into the midrange, the RX 9070's strong raster performance gives it the edge over the RTX 5070, but it's close. And the 5070 has the full arsenal of DLSS 4.5 features at its disposal in virtually every modern game.

Given the image quality and smoothness advantage of DLSS 4.5 upscaling and MFG, the 5070 got the nod in our most recent round of best graphics card picks after our 2026 retests.

The recently introduced RX 9070 GRE lands between the 5070 and the lower-end 5060 Ti and 9060 XT, a position for which Nvidia has no current-gen answer (and no easy path to one.) But AMD may have priced the GRE too high at $549, and we're already seeing big price drops on that product that might make it uniquely appealing in a market where the RTX 5060 Ti is its direct price competition.

The formerly midrange RTX 5060 Ti 16GB is hopelessly overpriced for the performance it offers in mid-2026, and the formerly somewhat affordable 8GB model is also selling for elevated prices now, making the entire 5060 Ti family impossible to recommend.

The RX 9060 XT 16GB holds down the entry-midrange 16GB position by itself, and its $459 price tag isn't so absurd as to make it un-recommendable. But as RX 9070 GREs start going on sale for $500-ish, the step up is going to look mighty tempting.

As we move further down the stack, the $300-ish Arc B580 represents Intel's best card right now, but it remains very much a budget part in mid-2026, trading blows with the RTX 3060 Ti and RTX 4060. The only reason it lands as high as it does in our results is that its 12GB of VRAM prevents its performance from totally plummeting at 4K, as it does for those older 8GB cards.

The RTX 5060 is 24% faster than the Arc B580 at 1080p and 17% faster at 1440p, and its prices are proportionately higher. The scarce RX 9060 XT 8GB is a whole 30% faster at 1080p and 23% faster at 1440p, for similarly more money.

We don't think the potential longevity afforded by the B580's 12GB of VRAM is worth the massive performance tradeoff versus current-gen 8GB GPUs. Save up a few more bucks and grab an RTX 5060 or RX 9060 8GB instead (and enjoy the more widely adopted DLSS 4.5 or FSR 4 versus XeSS while you're at it).

The $250 Arc B570 is still a good value in mid-2026, and as the only modern, decent graphics card available for (well) under $300, it's still worth a look if you can't stretch to an RTX 5050. But recent software ills that took months to fix have led us to consider it more of a gamble than a product worthy of a recommendation, even though those issues did eventually get corrected.

Ray Tracing GPU Benchmarks 2026

For 2026, we're testing a range of RT titles that present a progressively more difficult workload to the GPUs under test.

Grand Theft Auto V Enhanced, DOOM: The Dark Ages, and Indiana Jones and the Great Circle are all relatively lightweight RT titles that will run on a wide range of RT-ready hardware. Spider-Man 2, Cyberpunk 2077, and Assassin's Creed Shadows represent greater challenges to compute resources, VRAM, or both. And Black Myth Wukong and Alan Wake II still bring even the most powerful graphics cards to their knees at native resolutions.

Our results for those more demanding games represent more of a jumping-off point for upscaling and framegen rather than native performance. But lighter RT titles mean that you can explore the differences in image quality that ray tracing offers without crushing your GPU flat.

In any case, graphics cards at the top of our RT tests are ready for practically any RT game. But as you'll see, that's still quite the high bar to clear in 2026.

Ray Tracing GPU Benchmarks, Key Findings and Notes

• The RTX 5090 really comes into its own with RT enabled, as expected.
• AMD's RX 9070 XT outperforms the RX 7900 XTX in our RT tests, showing that the ray-tracing improvements in RDNA 4 deliver.
• Even with those improvements, the RX 9070 XT can only manage a tie with the RTX 4070 Ti with RT in play, coming in eighth place overall.
• Maintaining 60 FPS in RT titles (as a good foundation for upscaling and framegen enhancements) is a very tall order. Among recent products, you'll want:
  • an RTX 5060 Ti 16GB, RTX 5070, or RX 9070 GRE to handle ~60 FPS RT at 1080p without running out of VRAM
  • An RTX 4070, an RTX 5070 or RX 9070 at 1440p, or an RX 9070 XT or RTX 5070 Ti for the best possible experience
  • An RTX 5080, RTX 4090, or RTX 5090 at 4K

Ray Tracing GPU Benchmarks Rankings 2026

The RTX 5090 and RTX 4090 lead our RT results. But if you want to enjoy high-end RT without a 4090- or 5090-sized bankroll, the RTX 5070 Ti proves itself as the last card with a reasonable price-to-performance ratio before things get crazy.

The 5070 Ti leads the RX 9070 XT by a wide margin in our RT tests, and it's anywhere from 10%-15% behind the absurdly overpriced RTX 5080 across the board. But if you want the absolute best RT performance possible without stepping up to a 4090 or 5090, the extra cash for a 5080 is your best — and only — option.

That said, you should really explore DLSS 4.5 upscaling and MFG on the 5070 Ti before spending big to step up to a 5080.

The RX 9070 XT remains AMD's fastest RT GPU, outpacing the 7900 XTX across all tested resolutions. It leads the RDNA 3 halo card by about 10% across the board, which is an impressive generational leap considering its 33% deficit in CUs versus the XTX.

But the 9070 XT can only trade blows with the RTX 4070 Ti and RTX 5070 with Nvidia competition in the picture. It beats out the 5070 at 1080p and 1440p, but DLSS 4.5 upscaling and MFG give Blackwell gamers more flexible options for boosting performance with only minimal impact to image quality versus both RDNA 4 (and Ada).

Of course, the RX 9070 XT has FSR 4 upscaling and framegen at its disposal in compatible titles, but support for those features isn't as widespread as DLSS.

Meanwhile, the RX 9070 comes in slightly behind the RTX 5070 at 1080p and 1440p for RT. Even though its 16GB of VRAM prevents the 9070's 4K RT performance from completely nosediving, the average frame rate at that resolution isn't high enough to make it a 4K RT choice.

At 1080p, the RX 9070 GRE presents an intriguing RT option for Radeon gamers, delivering a near-60-FPS average baseline that would make a great FSR 4 foundation. But its 12GB of VRAM quickly becomes a liability at 1440p versus the 9070, and 4K is out of the question.

Overall, our demanding 2026 RT test suite shows that the bar for entry to a good RT experience remains high, especially as elevated prices on the RTX 5060 Ti 16GB mean that the first "real" RT-ready Blackwell card at 1080p and up is the RTX 5070. And on the Radeon side, you really want an RX 9070 GRE or RX 9070 for the best experience.

Test Setup and Hardware for GPU Benchmarks

We've used several different PCs for our testing over the years. The latest update switches to an AMD Zen 5 processor: the unparalleled Ryzen 7 9800X3D and its 3D V-Cache-enhanced performance.

Here are the specifications for our latest GPU test PC.

Tom's Hardware 2026 GPU Benchmarks Testbed

CPU: AMD Ryzen 7 9800X3D
CPU Cooler: Thermalright Phantom Spirit 120 SE
Memory: G.Skill Trident Z5 32GB (2x16GB) DDR5-6000 CL30
Motherboard: Asus TUF Gaming X670E-Plus Wifi
SSD: Inland Performance Plus 4TB
PSU: MSI MPG Ai1600TS 1600W

We test across the three most common gaming resolutions, 1080p, 1440p, and 4K, using a mix of high and ultra settings, depending on the title. Where possible, we use 'reference' cards for all of these tests, like Nvidia's Founders Edition models and AMD's reference designs. Most midrange and lower GPUs don't get reference models, however, and in some cases we only have factory-overclocked cards for testing. We do our best to select cards that are close to the reference specs in such cases.

For each graphics card, we follow the same testing procedure. We run one pass of each benchmark to "warm up" the GPU after launching the game, then perform our actual test runs across each resolution.

We carefully review our test data and check for anomalies. For example, we always expect the RTX 5080 to be faster than the RTX 5070 Ti. If it's not, and we're not in a CPU limited situation, we'll recheck both cards to ensure that our standings our accurate. We also check and retest in cases of subtler issues, as when a transient hitch or frame-time spike causes a large dip in 1% low FPS.

Due to the length of time required for testing each GPU, updated drivers and game patches inevitably come out that can impact performance. We periodically retest a few sample cards to verify our results are still valid, and if not, we go through and retest the affected game(s) and GPU(s). We may also add games to our test suite over time, if one comes out that is popular and conducive to testing. See what makes a good game benchmark for our selection criteria.

GPU Benchmarks: Individual Game Charts

The above tables provide a summary of performance, but for those that want to see the individual game charts, for both the standard and ray tracing test suites, we've got those as well. These charts only cover current-gen GPUs for readability.

These charts are up to date as of June 2026.

GPU Benchmarks — 1080p

GPU Benchmarks — 1440p

GPU Benchmarks — 4K

GPU Benchmarks — Power, Clocks, and Temperatures

Most of our discussion has focused on performance, but for those interested in power and other aspects of the GPUs, here are the appropriate charts. We'll run these from highest to lowest settings, as 4K ultra tends to be the most strenuous workload on most of these GPUs.

If you're looking for the legacy GPU hierarchy, head over to page two! We moved it to a separate page to help improve load times in our CMS as well as for the main website. And if you're looking to comment on the GPU benchmarks hierarchy, head over to our forums and join the discussion!

Choosing a Graphics Card

Choosing a Graphics Card

Choosing a Graphics Card

Which graphics card do you need? To help you decide, we created this GPU benchmarks hierarchy, comprising 48 GPUs from three generations of hardware for Nvidia and AMD and also including Intel's Battlemage cards. Not surprisingly, the fastest cards are using the latest GPU architectures, though they're not always a major upgrade over the prior generation.

Of course, it's not just about playing games. Many applications use the GPU for other work. But a good graphics card for gaming will typically do equally well in complex GPU computational workloads. Buy one of the top cards and you can run games at high resolutions and frame rates with the effects turned all the way up, and you'll be able to do content creation work as needed. Drop down to the middle and lower portions of the list and you'll need to start dialing down the settings to get acceptable performance in regular gameplay and GPU benchmarks.

If your main goal is gaming, you can't forget about the CPU. Getting the best possible gaming GPU won't help you much if your CPU is underpowered or many years out of date. So be sure to check out the Best CPUs for gaming page, as well as our CPU Benchmarks Hierarchy to make sure you have the right CPU for the level of gaming performance you're looking to achieve.

And don't forget about your monitor. Be sure to get a display whose refresh rate range matches the average frame rates of the graphics card that you want in the games that you play. Check out our list of the best gaming monitors for a starting point.

GPU Benchmarks from 2022–2024

Our 'legacy' GPU benchmarks used different hardware. Here are the details for the 2022–2024 configuration that used an Alder Lake 12900K CPU.

Tom's Hardware 2022–2024 GPU Testbed

Intel Core i9-12900K
MSI Pro Z690-A WiFi DDR4
Corsair 2x16GB DDR4-3600 CL16
Crucial P5 Plus 2TB
Cooler Master MWE 1250 V2 Gold
Cooler Master PL360 Flux
Cooler Master HAF500
Windows 11 Pro 64-bit

We have upgraded our test system and changed our test suite for 2025, effectively resetting our benchmarking and rankings. However, the old data — collected using a Core i9-12900K PC — remains valid. We aren't testing new GPUs with the old testbed, as that would double the time required for what is already a lengthy process, but we felt it would be helpful to some to keep the final data available.

We also had an interim system that never quite got wrapped up, which had a Core i9-13900K Raptor Lake CPU. Our original CPU was one of the impacted units that failed over time, which explains in retrospect why so much of the data felt a bit questionable. Thankfully, the new Ryzen 7 9800X3D system seems to be running just fine. Here are the 12900K results.

For these GPU benchmarks, we tested nearly every GPU released between 2016 and 2024, plus a few extras. All graphics cards were tested at 1080p medium and 1080p ultra, and we sorted the table by the 1080p ultra results. Where it made sense, we also tested at 1440p ultra and 4K ultra. All the scores are scaled relative to the top-ranking 1080p ultra card of the time, which in our suite is the RTX 4090 — especially at 4K and 1440p.

The above summary charts show the relative performance of the cards we've tested across the past several generations of hardware at 1080p ultra — swipe through the above gallery if you want to see the 1080p medium, 1440p, and 4K ultra images. There are a few missing options (e.g., the GT 1030, RX 550, and several Titan cards), but otherwise it's basically complete. Note that we also have data in the table below for some of the other older GPUs.

The eight games used for our standard GPU benchmarks hierarchy are Borderlands 3 (DX12), Far Cry 6 (DX12), Flight Simulator (DX11 Nvidia, DX12 AMD/Intel), Forza Horizon 5 (DX12), Horizon Zero Dawn (DX12), Red Dead Redemption 2 (Vulkan), Total War Warhammer 3 (DX11), and Watch Dogs Legion (DX12). The fps score is the geometric mean (equal weighting) of the eight games. Note that the specifications column links directly to our original review for the various GPUs.

*: GPU couldn't run all tests, so the overall score is slightly skewed at 1080p ultra.

While the RTX 4090 does technically take first place at 1080p ultra, it's the 1440p and especially 4K numbers that impress. It's less than 2% faster than the RTX 4080 Super at 1080p ultra, but that increases to 9% at 1440p and then 25% at 4K. Also note that the fps numbers in our table incorporate both the average and minimum fps into a single score — with the average given more weight than the 1% low fps.

Again, keep in mind that we're not including any ray tracing or DLSS results in the above table, as we use the same test suite with the same settings on all current and previous generation graphics cards. Since only RTX cards support DLSS (and RTX 40-series if you want DLSS 3), that would drastically limit which cards we could directly compare. You can see DLSS 2/3 and FSR 2 upscaling results in our RTX 4070 review if you want to check out how the various upscaling modes can help.

The RTX 4090 comes at a steep price, though on paper it's not that much worse than the previous generation RTX 3090. In fact, we'd say it's a lot better in some respects, as the 3090 was only a minor improvement in performance compared to the 3080 at the time of launch, but with more than double the VRAM. Nvidia pulled out all the stops with the 4090, increasing the core counts, clock speeds, and power limits to push it beyond all contenders. There are two problems with the 4090, however: It's not available at MSRP any longer, due to demand from the AI sector — it often costs $2,000 or more — and there are still concerns with pulling 450W of power over the 16-pin connector.

Stepping down from the RTX 4090, the RTX 4080 Super and RX 7900 XTX trade blows at higher resolutions, while CPU bottlenecks come into play at 1080p. We'll be switching our testbed in the near future, with the current results from our 13900K testing in the charts at the bottom of the page.

Outside of the latest releases from AMD and Nvidia, the RX 6000- and RTX 30-series chips still perform reasonably well and if you're using such a card, there may not be any need to upgrade at present. Intel's Arc GPUs also fall into this category and are something of a wild card.

We've been testing and retesting GPUs periodically, and the Arc chips running the latest drivers now complete all of our benchmarks without any major anomalies. (Minecraft was previously a problem, though Intel has finally sorted that out.) They're not great on efficiency, but overall performance and pricing for the A750 is quite good.

Turning to the previous generation GPUs, the RTX 20-series and GTX 16-series chips end up scattered throughout the results, along with the RX 5000-series. The general rule of thumb is that you get one or two "model upgrades" with the newer architectures, so for example the RTX 2080 Super comes in just below the RTX 3060 Ti, while the RX 5700 XT basically matches the newer and less expensive RX 6600 XT.

Go back far enough and you can see how modern games at ultra settings severely punish cards that don't have more than 4GB VRAM. We've been saying for a few years now that 4GB was just scraping by, and these days we'd avoid buying anything with less than 8GB of VRAM — 12GB or more is the minimum we'd want with a mainstream GPU, and 16GB or more for high-end and above. Old cards like the GTX 1060 3GB and GTX 1050 actually failed to run some of our tests, which skews their results a bit, even though they do better at 1080p medium.

Now let's switch over to the ray tracing hierarchy.

Ray Tracing GPU Benchmarks 2022–2024

Enabling ray tracing, particularly with demanding games like many of those we're using in our DXR test suite, can cause framerates to drop off a cliff. We're testing with "medium" and "ultra" ray tracing settings. Medium generally means using the medium graphics preset but turning on ray tracing effects (set to "medium" if that's an option; otherwise, "on"), while ultra turns on all of the RT options at more or less maximum quality.

Because ray tracing is so much more demanding, we're sorting these results by the 1080p medium scores. That's also because the RX 6500 XT, RX 6400, and Arc A380 basically can't handle ray tracing even at these settings, and testing at anything more than 1080p medium would be fruitless.

The five ray tracing games used are Bright Memory Infinite, Control Ultimate Edition, Cyberpunk 2077, Metro Exodus Enhanced, and Minecraft — all of these use the DirectX 12 / DX12 Ultimate API. The fps score is the geometric mean (equal weighting) of the five games, and the percentage is scaled relative to the fastest GPU in the list, which again is the GeForce RTX 4090.

If you want to see what the future may hold with ray tracing, check out our Alan Wake 2 benchmarks where the full path tracing barely manages playable performance even with upscaling on non-Nvidia GPUs. However, and this is a critical point, the number of games where RT truly makes a striking difference in visuals is extremely limited. For most games, we still feel running pure rasterization rendering makes more sense.

If you felt the RTX 4090 performance was impressive at 4K in our standard test suite, just take a look at the results with ray tracing. Nvidia put even more ray tracing enhancements into the Ada Lovelace architecture, and those start to show up here. There are still further potential performance improvements for ray tracing with SER, OMM, and DMM — not to mention DLSS 3, though that ends up being a bit of a mixed bag, since the generated frames don't include new user input and add latency.

If you want a real kick in the pants, we also ran many of the faster ray tracing GPUs through Cyberpunk 2077's RT Overdrive mode, which implements full "path tracing" (full ray tracing, without any rasterization) — as well as Alan Wake 2, which uses path tracing at higher settings, and Black Myth: Wukong that supports full ray tracing. Those games provide a glimpse of how future games could behave, and why upscaling and AI techniques like frame generation are here to stay.

Even at 1080p medium, a relatively tame setting for DXR (DirectX Raytracing), the RTX 4090 roars past all contenders and leads the previous generation RTX 3090 Ti by 41%. At 1080p ultra, the lead grows to 53%, and it's nearly 64% at 1440p. Nvidia made claims before the RTX 4090 launch that it was "2x to 4x faster than the RTX 3090 Ti" — factoring in DLSS 3's Frame Generation technology — but even without DLSS 3, the 4090 is 72% faster than the 3090 Ti at 4K.

AMD continued to relegate DXR and ray tracing to secondary status, focusing more on improving rasterization performance — and on reducing manufacturing costs through the use of chiplets on the new RDNA 3 GPUs. As such, the ray tracing performance from AMD isn't particularly impressive. The top RX 7900 XTX basically matches Nvidia's previous generation RTX 3080 12GB, which puts it barely ahead of the RTX 4070 — and that's not even in all DXR games. There are some minor improvements for RT performance in RDNA 3, though, as the 7800 XT for example ends up basically tied with the RX 6800 XT in rasterization performance but is 10% faster in DXR performance.

Intel's Arc A7-series parts show a decent blend of performance in general, with the A750 coming in ahead of the RTX 3060 overall. With the latest drivers (and with vsync forced off in the options.txt file), Minecraft performance also looks much more in line with the other Arc DXR results.

You can also see what DLSS Quality mode did for performance in DXR games on the RTX 4090 in our review, but the short summary is that it boosted performance by 78% at 4K ultra. DLSS 3 frame generation improved framerates another 30% to 100% in our testing, though we recommend exercising (extreme) caution when looking at FPS with the feature enabled. It can boost framerates in benchmarks, but when actually playing games it often doesn't feel much faster than without the feature.

Overall, with DLSS 2, the 4090 in our ray tracing test suite is nearly four times as fast as AMD's RX 7900 XTX. Ouch. AMD's FSR 2 and FSR 3 can help as well, and AMD continues to work on increasing the rate of adoption, but it still trails DLSS both in the number of games supported and in the overall image quality. Only two of the games in our DXR suite have FSR2 support. By comparison, all of the DXR games we're testing support DLSS2 — and one also supports DLSS3.

Without FSR2, AMD's fastest GPUs can only clear 60 fps at 1080p ultra, while remaining decently playable at 1440p with 40–50 fps on average. But native 4K DXR remains out of reach for just about every GPU, with only the 3090 Ti and above breaking the 30 fps mark on the composite score — and a couple of games still come up short on the 3090 Ti.

AMD also has FSR 3 frame generation. Like DLSS3, it adds latency, and AMD requires the integration of Anti-Lag+ support in games that use FSR 3. But Anti-Lag+ only works with AMD GPUs, which means non-AMD cards will likely incur a larger latency penalty. We've tested it in Avatar: Frontiers of Pandora and found it worked pretty well, but that was not the case in Forspoken and Immortals of Aveum. It has since gained a lot more traction, though quality and latency remain quite variable — it can look and run well in one game, and then fall flat in another.

The midrange GPUs like the RTX 3070 and RX 6700 XT basically manage 1080p ultra and not much more, while the bottom tier of DXR-capable GPUs barely manage 1080p medium — and the RX 6500 XT can't even do that, with single digit framerates in most of our test suite, and one game that wouldn't even work at our chosen "medium" settings. (Control requires at least 6GB VRAM to let you enable ray tracing.)

Intel's Arc A380 ends up just ahead of the RX 6500 XT in ray tracing performance, which is interesting considering it only has 8 RTUs going up against AMD's 16 Ray Accelerators. Intel posted a deep dive into its ray tracing hardware, and Arc seems reasonably impressive, except for the fact that the number of RTUs severely limits performance. The top-end A770 still only has 32 RTUs, which proves sufficient for it to pull ahead (barely) of the RTX 3060 in DXR testing, but it can't go much further than that. Arc A750 and above also ends up ahead of AMD's RX 6750 XT in DXR performance, showing just how poor AMD's RDNA 2 hardware is when it comes to ray tracing.

It's also interesting to look at the generational performance of Nvidia's RTX cards. The slowest 20-series GPU, the RTX 2060, still outperforms the newer RTX 3050 by a bit, but the fastest RTX 2080 Ti comes in a bit behind the RTX 3070. Where the 2080 Ti basically doubled the performance of the 2060, the 3090 delivers about triple the performance of the 3050.

Test System and How We Test for GPU Benchmarks

For each graphics card, we follow the same testing procedure. We run one pass of each benchmark to "warm up" the GPU after launching the game, then run at least two passes at each setting/resolution combination. If the two runs are basically identical (within 0.5% or less difference), we use the faster of the two runs. If there's more than a small difference, we run the test at least twice more to determine what "normal" performance is supposed to be.

We also look at all the data and check for anomalies, so for example RTX 3070 Ti, RTX 3070, and RTX 3060 Ti all generally going to perform within a narrow range — 3070 Ti is about 5% faster than 3070, which is about 5% faster than 3060 Ti. If we see games where there are clear outliers (i.e. performance is more than 10% higher for the cards just mentioned), we'll go back and retest whatever cards are showing the anomaly and figure out what the "correct" result would be.

Due to the length of time required for testing each GPU, updated drivers and game patches inevitably will come out that can impact performance. We periodically retest a few sample cards to verify our results are still valid, and if not, we go through and retest the affected game(s) and GPU(s). We may also add games to our test suite over the coming year, if one comes out that is popular and conducive to testing — see our what makes a good game benchmark for our selection criteria.

GPU Benchmarks: Individual Game Charts

The above tables provide a summary of performance, but for those that want to see the individual game charts, for both the standard and ray tracing test suites, we've got those as well. We're only including more recent GPUs in these charts, as otherwise things get very messy. These are also using a newer test suite and a 13900K CPU, which changes the performance slightly from the above table, simply because our newest tests are more relevant (but haven't been run on a lot of the older GPUs shown in the tables).

These charts were up to date as of November 11, 2024.

GPU Benchmarks — 1080p Medium

GPU Benchmarks — 1080p Ultra

GPU Benchmarks — 1440p Ultra

GPU Benchmarks — 4K Ultra

GPU Benchmarks — Power, Clocks, and Temperatures

Most of our discussion has focused on performance, but for those interested in power and other aspects of the GPUs, here are the appropriate charts.

Our even older 2020–2021 'legacy' GPU benchmarks used another set of hardware with different games. Here are the details for the Coffee Lake 9900K and Z390 PC.

Tom's Hardware 2020–2021 GPU Testbed

Intel Core i9-9900K
Corsair H150i Pro RGB
MSI MEG Z390 Ace
Corsair 2x16GB DDR4-3200
XPG SX8200 Pro 2TB
Windows 10 Pro (21H1)

The results below combine results from nine games with six resolution and setting combinations. All of the scores are combined (via a geometric mean calculation) into a single overall result, which tends to penalize the fastest and slowest GPUs — CPU bottlenecks come into play at 1080p medium, while VRAM limitations can kill performance at 4K ultra.

These results have not been updated since early 2022, when we added the RTX 3050 and RX 6500 XT to the list. We won't be adding future GPUs to this table, so there's no RTX 40-series, RX 7000-series, Arc, 3090 Ti, 6950 XT, 6750 XT, or 6650 XT, but it does help to provide a look at a slightly less demanding suite of games, where 6GB or more VRAM isn't generally required at 1080p ultra settings. You can use these older results to help inform your purchase decisions, if you don't typically run the latest games at maxed out settings.