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Nvidia RTX 5080: Content Creation, Professional Apps, and AI
Modern GPUs aren't just about gaming. They're used for video encoding and professional applications, and increasingly, they're being used for AI. We've revamped our professional and AI test suite to give a more detailed look at the various GPUs. We'll start with the AI benchmarks, as those tend to be more important for a wider range of users.
Procyon has several different AI tests, and we've run the AI Vision benchmark along with two different Stable Diffusion image generation tests. The tests have several variants available that are all determined to be roughly equivalent by UL: OpenVINO (Intel), TensorRT (Nvidia), and DirectML (for everything, but mostly AMD). There are also options for FP32, FP16, and INT8 data types, which can give different results. We tested the available options and used the best result for each GPU.
Procyon needs to be updated to work on the RTX 50-series GPUs, so for now,, we'll skip commenting on these tests. The 5080 and 5090 fail to run any of the TensorRT workloads we've tried. Nvidia is aware of the problem, but we're not sure how long it will take for a fix to become available.
ML Commons' MLPerf Client 0.5 test suite does AI text generation in response to a variety of inputs. There are four different tests, all using the LLaMa 2 7B model, and the benchmark measures the time to first token (how fast a response starts appearing) and the tokens per second after the first token. These are combined using a geometric mean for the overall scores, which we report here.
While AMD, Intel, and Nvidia are all ML Commons partners and were involved with creating and validating the benchmark, it doesn't seem to be quite as vendor-agnostic as we would like. AMD and Nvidia GPUs only have a DirectML execution path, while Intel has both DirectML and OpenVINO as options. Intel's Arc GPUs score quite a bit higher with OpenVINO than with DirectML.
The RTX 5080 delivers a good result here. It's not quite as fast as the RTX 4090, but it's 16% faster than the 4080 Super. Basically, the delta is on the higher end of what we saw in our gaming benchmarks. The additional memory bandwidth on the 5080 is the primary factor, as MLPerf isn't using FP4 for these tests.
We'll have some additional SPECworkstation 4.0 results below, but there's an AI inference test composed of ResNet50 and SuperResolution workloads that runs on GPUs (and potentially NPUs, though we haven't tested that). We calculate the geometric mean of the four results given in inferences per second, which isn't an official SPEC score but it's more useful for our purposes.
The RTX 5080 ends up 13% faster than the RTX 4080 Super in this test. It's still slower than the 4090, though, which is expected.
For our professional application tests, we'll start with Blender Benchmark 4.3.0, which has support for Nvidia Optix, Intel OneAPI, and AMD HIP libraries. Those aren't necessarily equivalent in terms of the level of optimizations, but each represents the fastest way to run Blender on a particular GPU at present.
Blender showed some massive gains in performance on the 5090 compared to the 4090. The 5080 on the other hand only shows a minor improvement over the 4080 Super. Overall, it's just over 5% faster. The result seems to be based on compute more than bandwidth — the 5090 is 35% faster than the 4090, and it has 27% more theoretical compute (with real-world clock speeds potentially giving it several percent more performance).
SPECworkstation 4.0 has two other test suites that are of interest in terms of GPU performance. The first is the video transcoding test using HandBrake, a measure of the video engines on the different GPUs and something that can be useful for content creation work. We use the average of the 4K to 4K and 4K to 1080p scores. Note that this only evaluates speed of encoding, not image fidelity.
The 5090 and 5080 have the same video encoding capabilities, so it's no surprise that they have basically the same result. The same goes for the 4090 and 4080 Super — the 4080 Super is clocked higher and thus does slightly better. AMD's results are faster, though quality in our previous GPU encoding testing was clearly lower. (Yes, we need to revisit that; just add it to the list of things to get done, please.)
Our final professional app tests consist of SPECworkstation 4.0's viewport graphics suite. This is basically the same tests as SPECviewperf 2020, only updated to the latest versions. (Also, Siemen's NX isn't part of the suite now.) There are seven individual application tests, and we've combined the scores from each into an unofficial overall score using a geometric mean.
AMD's drivers for its consumer cards tend to be more friendly toward these professional applications, leading to higher overall performance. The 5080 trades blows with the 4080 Super in these tests, with some favoring the 5080 and others favoring the 4080 Super.
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