Next, let us get to power consumption before getting to our final words. One of the great things is that there is no longer a need for a more complex PCIe architecture for a VDI card like this. Here are eight M40 GPUs, using two M40 PCIe cards (four GPUs per card): NVIDIA GRID M40 GPU – nvidia-smi topo m This is certainly different from the NVIDIA M40‘s that had four GPUs per card from a few generations prior. As we can see, we simply have an 8x PCIe link topology. While we did not have NVLINK bridges, here is what eight of these GPUs look like in an AMD EPYC system without PCIe switches. Instead, they tend to sell for much less than a NVIDIA A100 SXM4 solution, while at the same time providing vGPU features for solutions such as VDI/ virtual workstations. Still, the real reason one uses NVIDIA A40’s is not necessarily for the training performance. In terms of performance, this is a rough guide, but a PCIe NVIDIA A100 doing training will be around twice as fast, and the top-end SXM4 80GB 500W A100’s as we tested in Liquid Cooling Next-Gen Servers Getting Hands-on with 3 Options are roughly about 2.4-2.5x as fast with something smaller like ResNet-50 training but that delta can go up as one uses more memory and NVLINK with larger models. ASUS ESC8000A E11 GPU Performance Compared To BaselineĪlso, the Tyan Thunder HX FT83A-B7129 saw variances across our quick deep learning benchmarks. We noticed slight variances between either GPUs, or GPUs in the larger 8x and 10x GPU systems that we reviewed such as the ASUS ESC8000A-E11. In terms of performance, the NVIDIA A40 has been out for quite some time at this point, but we just wanted to show a different view than is publicly out there using multi-GPU systems.
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