sglang/docs_new/docs/references/torch_compile_cache.mdx

---
title: "Enabling cache for torch.compile"
metatags:
    description: "SGLang torch.compile cache: TORCHINDUCTOR_CACHE_DIR for faster deployment across multiple machines."
---
SGLang uses `max-autotune-no-cudagraphs` mode of torch.compile. The auto-tuning can be slow.
If you want to deploy a model on many different machines, you can ship the torch.compile cache to these machines and skip the compilation steps.

This is based on https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html


1. Generate the cache by setting TORCHINDUCTOR_CACHE_DIR and running the model once.
```text Output
TORCHINDUCTOR_CACHE_DIR=/root/inductor_root_cache python3 -m sglang.launch_server --model meta-llama/Llama-3.1-8B-Instruct --enable-torch-compile
```
2. Copy the cache folder to other machines and launch the server with `TORCHINDUCTOR_CACHE_DIR`.