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2.2 KiB
2.2 KiB
Apple Silicon with Metal (MLX)
This document describes how run SGLang on Apple Silicon using Metal (MLX). If you encounter issues or have questions, please open an issue.
Install SGLang
You can install SGLang using one of the methods below.
Install from Source
# Use the default branch
git clone https://github.com/sgl-project/sglang.git
cd sglang
# Install sglang python package
pip install --upgrade pip
rm -f python/pyproject.toml && mv python/pyproject_other.toml python/pyproject.toml
uv pip install -e "python[all_mps]"
Launch of the Serving Engine
Launch the server with:
SGLANG_USE_MLX=1 python -m sglang.launch_server \
--model <MODEL_ID_OR_PATH> \
--disable-cuda-graph \
--host 0.0.0.0
Key Parameters Explained:
SGLANG_USE_MLX=1- Enables the use of MLX as the SGLang runtime backend (if disabled, SGLang will fall back totorch.mps, which has less support)--disable-cuda-graph- Disables usage of CUDA graph, which is not relevant for Apple Metal.--disable-overlap-schedule- Disables overlap scheduling (enabled/not present by default) achieved using MLX'sasync_eval()
Benchmarking with Requests
sglang.benchmark_one_batch calls the synchronous prefill/decode methods directly without going through the scheduler and the overlap code path.
sglang.benchmark_offline_throughput can toggle overlap scheduling as it uses the scheduler and the overlap code path by using the flag --disable-overlap-schedule.
Throughput Testing
Basic synchronous one batch throughput:
SGLANG_USE_MLX=1 python -m sglang.bench_one_batch \
--model-path <MODEL_ID_OR_PATH> \
--disable-cuda-graph \
--tp-size 1 \
--batch-size 1 \
--input-len 60 \
--output-len 10
Synchronous offline throughput:
SGLANG_USE_MLX=1 python -m sglang.bench_offline_throughput \
--model-path <MODEL_ID_OR_PATH> \
--disable-cuda-graph \
--num-prompts 1 \
--disable-overlap-schedule
Asynchronous offline throughput:
SGLANG_USE_MLX=1 python -m sglang.bench_offline_throughput \
--model-path <MODEL_ID_OR_PATH> \
--disable-cuda-graph \
--num-prompts 1