[docs] added torch.compile cache to dpsk manual

docs/references/deepseek.md

@@ -10,6 +10,10 @@ SGLang is recognized as one of the top engines for [DeepSeek model inference](ht
 
 If you encounter errors when starting the server, ensure the weights have finished downloading. It's recommended to download them beforehand or restart multiple times until all weights are downloaded. Please refer to [DeepSeek V3]([https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#installation--launch](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base#61-inference-with-deepseek-infer-demo-example-only)) offical guide to download the weights.
 
+### Caching `torch.compile`
+
+The DeepSeek series have huge model weights, it takes some time to compile the model with `torch.compile` for the first time if you have added the flag `--enable-torch-compile`. By default, `torch.compile` will automatically cache the FX graph and Triton in `/tmp/torchinductor_root`, which might be cleared according to the [system policy](https://serverfault.com/questions/377348/when-does-tmp-get-cleared). You can export the environment variable `TORCHINDUCTOR_CACHE_DIR` to save compilation cache in your desired directory to avoid unwanted deletion. You can also share the cache with other machines to reduce the compilation time. You may refer to the [PyTorch official documentation](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) and [SGLang Documentation](./torch_compile_cache.md) for more details.
+
 ### Launch with One node of 8 H200
 
 Please refer to [the example](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#using-docker-recommended). **Note that Deepseek V3 is already in FP8. So we should not run it with any quantization arguments like `--quantization fp8 --kv-cache-dtype fp8_e5m2`.** Also, `--enable-dp-attention` can be useful to improve for Deepseek V3/R1's throughput. Please refer to [Data Parallelism Attention](https://docs.sglang.ai/references/deepseek.html#multi-head-latent-attention-mla-throughput-optimizations) for detail.