NVIDIA RTX 3090 FE 3DMark Suite Testing
Here we will run the GeForce RTX 3090 through graphics-related benchmarks.
In our 3DMark testing, it is clear to see the GeForce RTX 3090 crushes the previous-generation GPUs. In only a few instances, NVLINK configurations outperform a single GeForce RTX 3090 but at a higher power and heat load cost.
NVIDIA RTX 3090 FE Unigine Testing
The Unigine benchmarks are older and often do not perform well with current graphics cards. We can see this in the Unigine Valley graph. With the latest Superposition benchmark, the RTX 3090 FE easily takes the top spot.
Next, we are going to look at the GPU with several Deep Learning benchmarks.
Are you using the Tensorflow 20.11 container for all the machine learning benchmarks? It contains cuDNN 8.0.4, while the already released cuDNN 8.0.5 delivers significant performance improvements for the RTX 3090.
Great fp64 performance..
It’s not great fp64. The 3090’s AIDA64 GPGPU score of 638 is less than 10% of the 6351 FLOPS my Radeon Pro VII pulls down. https://twitter.com/hubick/status/1324203898949652480
How did the NVlinked, Titan RTXs and Quadro RTX 8000s get better than 100% scaling in OctaneRender 4.0?
Chris Hubick
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Misha, as well known AMD shill, is being facetious – and this is a graphics card and not a compute card like the Ampere A100 – which trades the RT cores for FP64…
Would love to see this dataset run on a A100 for comparison, is that review coming as well or are those datasets not public?
Hi,
I LOVE the GeForce and Threadripper compute reviews (especially the youtube video reviews!)
However, for our work load, we really need to know how the hardware performs for double-precision memory-bound algorithms.
The best benchmark that matches our problems (computational physics) is the HPCG benchmark.
Would it be possible to add HPCG results for the reviews? (http://www.hpcg-benchmark.org/)
Also, for some other computational physicists, having the standard LinPack benchmark (for compute-bound algorithms) would be really nice to see as well (https://top500.org/project/linpack/)
– Ron