Compiling a LLM for High Performance Inference
This is the next blog in the blog series on LLMs and Inference. In the previous blog on LLMs and Inference, we discussed about the safetensors format for LLMs. In this blog, we will walk through a critical step for LLM Inference.
Compiling a Large Language Model (LLM) generally refers to optimizing the model’s computational graph and kernel execution to improve inference or training performance on specific hardware (like GPUs or TPUs). Think of this as the next logical step that is performed after loading a model.
