A guide to open-source LLM inference and performance

Re:A guide to open-source LLM inference and performance by llwu on Monday November 20, 2023 @09:56PM (#38358443)

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?

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Re:A guide to open-source LLM inference and performance by varunshenoy on Monday November 20, 2023 @11:13PM (#38359109)

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].

[1] https://developer.nvidia.com/blog/cuda-refresher-cuda-progra...

[2] https://arxiv.org/pdf/2205.14135.pdf

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Re:A guide to open-source LLM inference and performance by Const-me on Tuesday November 21, 2023 @09:12AM (#38363830)

> How did we overcome the HBM <-> SRAM bottleneck?

Because every number we load from the model through that bottleneck gets reused, to compute different requests within the batch.

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