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 10, 14:00 UTC
[Doc] Fix: Correct vLLM announcing blog post link in docs (#31868)
Signed-off-by: enfinity <festusowumi@gmail.com>
Festus Ayobami Owumi committed
Jan 7, 2026 at 18:06 UTC
05f47bd8d2f3ccbe926af05d953c0f429b87b5b6
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docs/README.md
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@@ -62,7 +62,7 @@ vLLM is flexible and easy to use with:
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For more information, check out the following:
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-- [vLLM announcing blog post](https://vllm.ai) (intro to PagedAttention)
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+- [vLLM announcing blog post](https://blog.vllm.ai/2023/06/20/vllm.html) (intro to PagedAttention)
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- [vLLM paper](https://arxiv.org/abs/2309.06180) (SOSP 2023)
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- [How continuous batching enables 23x throughput in LLM inference while reducing p50 latency](https://www.anyscale.com/blog/continuous-batching-llm-inference) by Cade Daniel et al.
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- [vLLM Meetups](community/meetups.md)