A guide to open-source LLM inference and performance

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

> 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.

Thanks!

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Absolutely. Looks like the M1 Ultra has 800GB/s of memory bandwidth and ~20 TFLOPS of compute.

The same calculations from the post should hold, except with these new values.

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.

highly relevant question today