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[Doc] Tiny update description on torch compile (#17819)

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Baizhou Zhang
2026-01-27 18:59:04 +08:00
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parent c56d19b977
commit 832c756549
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7
docs/advanced_features/server_arguments.md
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@@ -50,15 +50,12 @@ You can find all arguments by `python3 -m sglang.launch_server --help`
```bash
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
```
- To enable `torch.compile` acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. By default, the cache path is located at `/tmp/torchinductor_root`, you can customize it using environment variable `TORCHINDUCTOR_CACHE_DIR`. For more details, please refer to [PyTorch official documentation](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) and [Enabling cache for torch.compile](https://docs.sglang.io/references/torch_compile_cache.html).
- To enable torchao quantization, add `--torchao-config int4wo-128`. It supports other [quantization strategies (INT8/FP8)](https://github.com/sgl-project/sglang/blob/v0.3.6/python/sglang/srt/server_args.py#L671) as well.
- To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.
- To enable fp8 kv cache quantization, add `--kv-cache-dtype fp8_e5m2`.
- To enable fp8 kv cache quantization, add `--kv-cache-dtype fp8_e4m3` or `--kv-cache-dtype fp8_e5m2`.
- To enable deterministic inference and batch invariant operations, add `--enable-deterministic-inference`. More details can be found in [deterministic inference document](../advanced_features/deterministic_inference.md).
- If the model does not have a chat template in the Hugging Face tokenizer, you can specify a [custom chat template](../references/custom_chat_template.md). If the tokenizer has multiple named templates (e.g., 'default', 'tool_use'), you can select one using `--hf-chat-template-name tool_use`.
- To run tensor parallelism on multiple nodes, add `--nnodes 2`. If you have two nodes with two GPUs on each node and want to run TP=4, let `sgl-dev-0` be the hostname of the first node and `50000` be an available port, you can use the following commands. If you meet deadlock, please try to add `--disable-cuda-graph`
- (Note: This feature is out of maintenance and might cause error) To enable `torch.compile` acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. By default, the cache path is located at `/tmp/torchinductor_root`, you can customize it using environment variable `TORCHINDUCTOR_CACHE_DIR`. For more details, please refer to [PyTorch official documentation](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) and [Enabling cache for torch.compile](https://docs.sglang.io/references/torch_compile_cache.html).
```bash
# Node 0
python -m sglang.launch_server \