Thank you, far better answer than mine! Those are indeed wild numbers, although interestingly "only" 23kw, I'd expect the same level of compute in GPUs to be quite a lot more than that, or at least higher power density.
> For 969 tok/s in int8, you need 392 TB/s memory bandwidth
I think that math is only valid for batch size = 1. When these 969 tokens/second come from multiple sessions of the same batch, loaded model tensor elements are reused to compute many tokens for the entire batch. With large enough batches, you can even saturate compute throughput of the GPU instead of bottlenecking on memory bandwidth.
Memory bandwidth for inferencing does not scale with the number of GPUs. Scaling instead requires more concurrent users. Also, I am told that 8 H100 cards can achieve 600 to 1000 tokens per second with concurrent users.
Inferencing is memory bandwidth bound. Add more GPUs on a batch size 1 inference problem and watch it run no faster than the memory bandwidth of a single GPU. It does not scale across the number of GPUs. If it could, you would see clusters of Nvidia hardware outperforming Cerebras’ hardware. That is currently a fantasy.
The limiting factor with GPU for inference is memory bandwidth. For 969 tok/s in int8, you need 392 TB/s memory bandwidth or 200 H100s.