An H100 supports 80 GB of memory. so at FP8 that would allow 3 of the 16+1 models per GPU (assuming around 26B per model), requiring 9 H100s, that usually would not fit one chassis I guess.
Once you have something with 192 GB it gets interesting. You could probably have 7 at FP8 per GPU. At FP16 it probably only would fit 3 per card, requiring 9 again.
I'd say for the current memory layout of cards they missed a little bit the sweet spot. With slightly smaller models or one expert less one should be able to run it on 8 H100s at FP8 or 2 B100s at FP8 or even on 4 B100s at FP16 if I calculated correctly.
Re:Tencent Hunyuan-Large
by bick_nyers on Wednesday November 06, 2024 @10:02AM
(#42063373)
You could always split one of the experts up across multiple GPUs. I tend to agree with your sentiment, I think researchers in this space tend to not optimize that well for inference deployment scenarios. To be fair, there is a lot of different ways to deploy something, and a lot of quantization techniques and parameters.
When splitting models layer by layer, users in r/LocalLLaMA have reported good results with as low as PCIE 3.0 x4 as the interconnect (4GB/s). For tensor parallelism, the interconnect requirements are higher but the upside can be faster speeds in accordance to number of GPUs split across (whereas layer by layer operated like a pipeline, so isn't necessarily faster than what a single GPU can provide, even if splitting across 8 GPUs).