Profiling is only intended for vLLM developers and maintainers to understand the proportion of time spent in different parts of the codebase. **vLLM end-users should never turn on profiling** as it will significantly slow down the inference.
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We support tracing vLLM workers using the `torch.profiler` module. You can enable tracing by setting the `VLLM_TORCH_PROFILER_DIR` environment variable to the directory where you want to save the traces: `VLLM_TORCH_PROFILER_DIR=/mnt/traces/`
The OpenAI server also needs to be started with the `VLLM_TORCH_PROFILER_DIR` environment variable set.
When using `benchmarks/benchmark_serving.py`, you can enable profiling by passing the `--profile` flag.
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Only enable profiling in a development environment.
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Traces can be visualized using <https://ui.perfetto.dev/>.