alt Hacker NewsThe largest nodes in his cluster each have 512GB RAM. DeepSeek V3.1 is a 671B parameter model whose weights take up 700GB RAM: https://huggingface.co/deepseek-ai/DeepSeek-V3.1
I would have expected that going from one node (which can't hold the weights in RAM) to two nodes would have increased inference speed by more than the measured 32% (21.1t/s -> 27.8t/s).
With no constraint on RAM (4 nodes) the inference speed is less than 50% faster than with only 512GB.
Am I missing something?
Replies
the TB5 link (RDMA) is much slower than direct access to system memory
Weights are read-only data so they can just be memory mapped and reside on SSD (only a small fraction will be needed in VRAM at any given time), the real constraint is activations. MoE architecture should help quite a bit here.
You only get 80Gbps network bandwidth. There's your bottleneck right there. Infiniband in comparison can give you up to x10 times that.