[Doc] Add documentation for vLLM continuous benchmarking and profiling (#25819)
Signed-off-by: Naman Lalit <nl2688@nyu.edu>
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@@ -823,6 +823,30 @@ The latest performance results are hosted on the public [vLLM Performance Dashbo
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More information on the performance benchmarks and their parameters can be found in [Benchmark README](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md) and [performance benchmark description](gh-file:.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md).
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### Continuous Benchmarking
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The continuous benchmarking provides automated performance monitoring for vLLM across different models and GPU devices. This helps track vLLM's performance characteristics over time and identify any performance regressions or improvements.
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#### How It Works
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The continuous benchmarking is triggered via a [GitHub workflow CI](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-benchmark.yml) in the PyTorch infrastructure repository, which runs automatically every 4 hours. The workflow executes three types of performance tests:
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- **Serving tests**: Measure request handling and API performance
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- **Throughput tests**: Evaluate token generation rates
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- **Latency tests**: Assess response time characteristics
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#### Benchmark Configuration
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The benchmarking currently runs on a predefined set of models configured in the [vllm-benchmarks directory](https://github.com/pytorch/pytorch-integration-testing/tree/main/vllm-benchmarks/benchmarks). To add new models for benchmarking:
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1. Navigate to the appropriate GPU directory in the benchmarks configuration
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2. Add your model specifications to the corresponding configuration files
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3. The new models will be included in the next scheduled benchmark run
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#### Viewing Results
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All continuous benchmarking results are automatically published to the public [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm).
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[](){ #nightly-benchmarks }
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## Nightly Benchmarks
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