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FP16 AI workloads are very important to AMD and Nvidia (AMD MI250X and Nvidia A100/H100 were really designed for this), and yet Tesla leapfrogged them with a mo
by mrb 4y ago
FP16 AI workloads are very important to AMD and Nvidia (AMD MI250X and Nvidia A100/H100 were really designed for this), and yet Tesla leapfrogged them with a more than 2x reduction in die area, and more features (eg. out of order exec). This is what's impressive. AMD, Nvidia, and even Intel, should have been leading this, but they weren't. Seems to be a classic innovator's dilemma.
- WithinReason 4y agoLet's compare with a competitor you never heard of: Nvidia RTX 3090: 285 Tensor-TOPs @ 350 W [1] Specialised accelerator: 100 TOPS at less than four watts [2] That's a 30x improvement over a GPU in power consumption. Sure, it's inference only, but training and inference are not that different. [1]: https://wccftech.com/nvidia-geforce-rtx-3090-geforce-rtx-3080-geforce-rtx-3070-graphics-cards-custom-model-roundup/ https://wccftech.com/nvidia-geforce-rtx-3090-geforce-rtx-308... [2]: https://www.imaginationtech.com/product/img-4nx-mc8/ https://www.imaginationtech.com/product/img-4nx-mc8/
- mrb 4y agoTOPS isn't indicative of actual performance https://semiengineering.com/lies-damn-lies-and-tops-watt/ https://semiengineering.com/lies-damn-lies-and-tops-watt/ Also, this IMG chip is vaporware and they can quote an arbitrarily high TOPS/W by trading voltage & frequency with die area, therefore increasing cost beyond reasonable. I'm not surprised IMG shares no other metric.
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