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How to evaluate performance of LLM inference frameworks
Inference Frameworks TL;DR - LLM inference frameworks have hit the “memory wall”, which is a hardware imposed speed limit on memory bound code. That means LLM application developers don’t need to worry about evaluating all of the nuances of different frameworks.
That operation of loading 400 billion weights makes LLM inference memory bound on most modern systems, e.g. GPUs with HBM. It uses a small neural network with fewer parameters to predict most tokens, and only calls in the bigger LLM to check it’s work and correct it when it makes a mistake. Until the next breakthrough comes along, be wary of claims from inference frameworks promising top performance as they may be trading off accuracy for speed or comparing against unoptimized baselines.
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