Startup is also working with Qualcomm on optimized models for its Cloud AI 100 Ultra inference chips
Compared to Nvidia's H100, the WSE-3 is roughly 57x larger and boasts roughly 62x the sparse FP16 performance. But considering the CS-3's size and power consumption, it might be more accurate to compare it to a with a total of 16 H100s inside. In this comparison, the CS-3 is still about 4x faster, but that's only when looking at sparse FP16 performance.
That's not to say Cerebras' systems are faster in every scenario. The company's performance claims rely heavily on sparsity. Considering the speed-up, we have a hard time imagining anyone would opt for Cerebras' infrastructure if they couldn't take advantage of sparsity, but even if you can't, it's pretty dang close.
Wednesday, Cerebras revealed that CG-3 was destined for Dallas, Texas, and would implement the newer CS-3 platform boosting the sites performance to 8 AI exaFLOPS. Assuming that the remaining six sites also feature 64 CS-3s, the nine-site cluster would actually boast 64 AI exaFLOPS of collective compute rather than the 36 exaFLOPS of sparse FP16 initially promised.
Under the partnership, Cerebras and Qualcomm will develop optimized models for the latter's AI 100 Ultra inference chips
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