Question on the "Batching memory-bound processes on a GPU" section - it says "This enables us to reuse parts of the model that we’ve already loaded into the GPU’s SRAM", but the 10 GB we are loading is into the HBM, right? How did we overcome the HBM <-> SRAM bottleneck?
More generally, how can we find out the size of the SRAM?
Good question. Yes, the 10GB available for batching is in the HBM. In a single forward pass, you move the entire model from HBM -> SRAM exactly once. In a batched forward pass, this is still the case, so you end up doing more compute for the same amount of memory movement.
You can calculate the SRAM as follows: an A100 has 108 SMs, and each SM has 192 KB in SRAM (shared memory, aka its L1 cache) [1]. Multiplied out, this is ~20 MB of total SRAM. This happens to match up with the diagram in the Flash Attention paper [2].
Thanks a lot for the material Varun, neat presentation with exhaustive computations that make it easy to follow. Question on the serving part: vLLM, Deepspeed, TensorRT-LLM... ? Thanks!
Slightly different set of trade-offs, but similar mental model. You always use large batch sizes (compute bound) and the bottleneck usually ends up communication between GPUs/nodes.
Likely trending on home page since this is directly relevant to LLM costs, i.e., questions like "how much would it cost to rebuild ChatGPT from scratch".
