Nvidia reportedly testing lower memory configs of Rubin Ultra as memory shortage bites back — designs tested include as little as 192 GB and step back to HBM4 [Updated]

Nvidia CEO presenting Rubin Ultra at GTC 2026.
(Image credit: Nvidia)
Recent updates

Nvidia has reached out in response to the report with the following statement:

"With NVIDIA Rubin Ultra, we are optimizing across compute, networking and memory to deliver the best performance and efficiency for our customers' AI deployments. These optimizations let us build more GPUs and deploy more AI systems."

Nvidia is reportedly testing variations of its upcoming Rubin Ultra accelerator with less memory due to concerns it won't be able to source enough HBM. Some versions include just 192 GB of memory and use HBM4 instead of HBM4E, as originally announced, according to The Information. The report confirms an earlier comment from firm SemiAnalysis about a potential Rubin Ultra memory downgrade.

We first saw Rubin Ultra in the flesh earlier this year at GTC, where Nvidia showed off a compute tray housing four compute chiplets alongside 1 TB of HBM4E memory. The accelerator is part of Nvidia's Kyber NVL144 design, which is set to roll out in 2027. SemiAnaylsis reported that the rack was delayed to 2028. "Our roadmap is intact," said Nvidia to Tom's Hardware in response, though the company made no clarification on if the delay was real or not. We've reached out to Nvidia regarding this latest report.

According to The Information, Nvidia is testing versions of Rubin Ultra with 192 GB or 256 GB of memory, as well as versions that use fewer than the 16 announced memory stacks. Perhaps most importantly, Nvidia is reportedly testing with HBM4, not HBM4E as originally announced. Along with the traditional improvements we see in each new HBM generation, HBM4E is unique in that it offers a customizable base logic die. Last year, Micron announced a partnership with TSMC to manufacture the base die and allow customers to tweak the logic die based on their needs.

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The complexity of HBM4E has reportedly caused a strain on supply, with memory manufacturers unable to keep pace with Rubin Ultra's rollout. At least three lower-memory designs have been tested by Nvidia, according to the report, though we don't have a full picture of details on those prototypes. The report claims testing with HBM4, as well as 192 GB and 256 GB configurations, though it makes no mention of the number of compute dies, nor the memory type for each tested capacity.

The number of dies is important. In June, reports circulated that Nvidia cancelled its quad-die Rubin Ultra design due to manufacturing complexities. Although Nvidia has yet to comment, reports at the time suggested Nvidia would move ahead with a dual-GPU Rubin Ultra. In such a case, less memory would make more sense. Even with a dual-die Rubin Ultra, the quoted capacities are lower than expected. Each base Rubin GPU currently ships with 288 GB of HBM4.

It's clear Nvidia is trying to get ahead with memory in a world where agreements have been signed multiple years into the future. Nvidia has several of its own agreements. In June, the company announced a partnership with SK hynix to develop next-generation memory technology, which includes HBM, but also LPDDR5X and DDR5. In July, Nvidia expanded that partnership with a $500 billion strategic relationship that includes a long-term memory supply agreement with SK.

Although Nvidia is considering lower-memory configurations, one Nvidia customer told The Information that per-GPU memory isn't a top concern, valuing the relationship with Nvidia over the long term.

Memory shortages are touching nearly every design currently on the market, though enterprise systems packing HBM are particularly vulnerable. Last week, Digitimes reported that Samsung, SK hynix, and Micron have sold through their HBM capacity through 2027. Last month, SK Hynix CEO Kwak Noh-jung said 2027 will be the "worst year" for the memory shortage, with supply constraints lasting through 2030.

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Jake Roach
Senior Analyst, CPUs

Jake Roach is the Senior CPU Analyst at Tom’s Hardware, writing reviews, news, and features about the latest consumer and workstation processors.

  • DS426
    You're telling me more AI is creating less AI?

    What a fun paradox.
    Reply
  • Jagar123
    Ah this quote from Jensen...

    “the fact that everything is scarce is fantastic for us.” - Link to VideoCardz article.

    Of course, he'll now spin this so his stock prices stay high. I am so over him greedily destroying the consumer market.
    Reply
  • usertests
    AI austerity builds character.
    Reply
  • S58_is_the_goat
    https://i.imgur.com/fXFUSst.png
    Reply
  • alan.campbell99
    Not sure if this is exactly shooting oneself in the foot but probably close to it.
    Reply
  • Vanderlindemedia
    Just start building with traditional GDDR3 and above. Make the bus 512 bits wide or even 1024 bits, and you got memory bandwidth.
    Reply
  • derekullo
    Jagar123 said:
    Ah this quote from Jensen...

    “the fact that everything is scarce is fantastic for us.” - Link to VideoCardz article.

    Of course, he'll now spin this so his stock prices stay high. I am so over him greedily destroying the consumer market.
    Jensen's job as CEO is to make Nvidia more profitable.
    He has done his job quite well!

    According to the paragraph on
    https://www.tomshardware.com/pc-components/gpus/nvidia-sells-rtx-50-series-gpus-at-msrp-during-quakecon-2026-graphics-cards-sold-at-launch-prices-more-than-a-year-after-release-are-now-considered-an-attraction
    5090 = 100% over MSRP
    5080 = 29% over MSRP
    5070Ti = 32% over MSRP
    5070 = 14% over MSRP

    The only real outlier is the 5090 since it can be more effectively used for AI workloads with its 32 gigabytes of ram.
    I'm still using a 3080Ti, but I'll probably build a new computer with a 6080 when it comes out and if the price is 29% over MSRP that isn't world-ending.

    It literally does pay to own Nvidia stock.
    Even if it's only 4-5% of your portfolio, if it is rebalanced often it can really raise the value of your other stocks/etfs.
    My Mom thought I was crazy investing in a "video game" company back in 2005 ... she now owns Nvidia shares too lol.
    Reply
  • call101010
    derekullo said:
    Jensen's job as CEO is to make Nvidia more profitable.
    He has done his job quite well!

    According to the paragraph on
    https://www.tomshardware.com/pc-components/gpus/nvidia-sells-rtx-50-series-gpus-at-msrp-during-quakecon-2026-graphics-cards-sold-at-launch-prices-more-than-a-year-after-release-are-now-considered-an-attraction
    5090 = 100% over MSRP
    5080 = 29% over MSRP
    5070Ti = 32% over MSRP
    5070 = 14% over MSRP

    The only real outlier is the 5090 since it can be more effectively used for AI workloads with its 32 gigabytes of ram.
    I'm still using a 3080Ti, but I'll probably build a new computer with a 6080 when it comes out and if the price is 29% over MSRP that isn't world-ending.

    It literally does pay to own Nvidia stock.
    Even if it's only 4-5% of your portfolio, if it is rebalanced often it can really raise the value of your other stocks/etfs.
    My Mom thought I was crazy investing in a "video game" company back in 2005 ... she now owns Nvidia shares too lol.
    Dream on it ... 6080 is not going to see the light soon. if at all .
    Reply
  • edzieba
    If the AI memory shortage is also hitting the companies buying the memry driving the AI memory shortage, where is it all going? Is Dr. Ian Cutress just sitting at the end of an SK Hynix line chowing down on tasty SDRAM wafers?
    call101010 said:
    Dream on it ... 6080 is not going to see the light soon. if at all .
    The same claims were made for the 4xxx and 3xxx series, too.
    Reply
  • usertests
    edzieba said:
    If the AI memory shortage is also hitting the companies buying the memry driving the AI memory shortage, where is it all going? Is Dr. Ian Cutress just sitting at the end of an SK Hynix line chowing down on tasty SDRAM wafers?
    Let's say the bubble is not popping. If there's a persistent memory shortage, that could lead to an excess of compute/logic (TSMC wafers). It might mean cheap CPUs for the consumer, even if they are often paired with little RAM.

    A large silicon, high SRAM product could shine. I don't think Cerebras Wafer Scale Engine is that product, because even though it's a giant chip with ~44 GB SRAM, it ends up being paired with terabytes of memory, same as everything else.

    Maybe it's time for someone to do 3D SRAM on a massive scale.
    Reply