Gigabyte Announces Two New Deep Learning Engines with Maximum GPU Density

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Assuming each of the GPUs runs at x16 lanes, I'm a little surprised there's enough PCIe lanes for all of that hardware. I would think Epyc would make for a more logical and cost effective choice in this particular setup. A single Epyc could handle 8 GPUs with x16 lanes. I know server-grade hardware tends to only use x8 lanes, but I'm sure these GPUs can soak up a lot of bandwidth.
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They use NVLink, and not PCIe lanes. The only PCIe lanes are for those expansion slots (10gigabit network and such)
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Nvidia had the original dgx now they have the dgx2. I'm guessing it would compete with that. However, unless it's considerably cheaper I would go with the dgx2 mainly because their solution is prob a bit more mature and works with their Tesla v100 gpu better. Cuz when I may looked the new dgx2 with 16 x v100 with 32 gb of hbm2 memory is about $400,000