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 4, 17:30 UTC
[Doc] fix capitalization consistency in README (vLLM, Hugging Face) (#40729)
Signed-off-by: Vinayak Mishra <vinayakmishra448@gmail.com>
Vinayak Kumar committed
Apr 24, 2026 at 07:53 UTC
eba73068ea861c2a76753ab82218b08176fce765
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README.md
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@@ -14,7 +14,7 @@ Easy, fast, and cheap LLM serving for everyone
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| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://blog.vllm.ai/"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
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</p>
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-🔥 We have built a vllm website to help you get started with vllm. Please visit [vllm.ai](https://vllm.ai) to learn more.
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+🔥 We have built a vLLM website to help you get started with vLLM. Please visit [vllm.ai](https://vllm.ai) to learn more.
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For events, please visit [vllm.ai/events](https://vllm.ai/events) to join us.
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@@ -50,7 +50,7 @@ vLLM is flexible and easy to use with:
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- Efficient multi-LoRA support for dense and MoE layers
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- Support for NVIDIA GPUs, AMD GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more.
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-vLLM seamlessly supports 200+ model architectures on HuggingFace, including:
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+vLLM seamlessly supports 200+ model architectures on Hugging Face, including:
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- Decoder-only LLMs (e.g., Llama, Qwen, Gemma)
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- Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS)