A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 15, 02:15 UTC
[Docs] remove outdated performance benchmark (#20935)
Signed-off-by: Kuntai Du <kuntai@uchicago.edu>
Kuntai Du committed
Jul 14, 2025 at 15:14 UTC
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README.md
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@@ -63,8 +63,6 @@ vLLM is fast with:
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- Speculative decoding
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- Chunked prefill
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-**Performance benchmark**: We include a performance benchmark at the end of [our blog post](https://blog.vllm.ai/2024/09/05/perf-update.html). It compares the performance of vLLM against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [SGLang](https://github.com/sgl-project/sglang) and [LMDeploy](https://github.com/InternLM/lmdeploy)). The implementation is under [nightly-benchmarks folder](.buildkite/nightly-benchmarks/) and you can [reproduce](https://github.com/vllm-project/vllm/issues/8176) this benchmark using our one-click runnable script.
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vLLM is flexible and easy to use with:
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- Seamless integration with popular Hugging Face models