## [Compiling a LLM for High Performance Inference](https://docs.rafay.co/blog/2025/04/24/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](https://docs.rafay.co/blog/2025/04/23/safetensors-the-secure-scalable-format-powering-llm-inference/) 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.