Native-speed vLLM transformers modeling backend

Native-speed vLLM transformers modeling backend Native-speed vLLM transformers modeling backend Published July 8, 2026 Update on GitHub Upvote 76 Harry Mellor hmellor Lysandre lysandre TL;DR : The transformers vLLM backend is now as fast (or faster) than custom vLLM implementations for many LLM architectures. Model authors can automatically leverage their transformers implementations to get ultra fast vLLM inference, for free.

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• Native-speed vLLM transformers modeling backend Published July 8, 2026 Update on GitHub Upvote 76 Harry Mellor hmellor Lysandre lysandre TL;DR : The transformers vLLM backend is now as fast (or faster) than custom vLLM implementations for many LLM architectures.
• Model authors can automatically leverage their transformers implementations to get ultra fast vLLM inference, for free.
• # Upgrade the vllm pip package uv pip install --upgrade vllm --torch-backend auto The transformers library has become the reference modeling library for Machine Learning.
• It supports 450+ architectures through consistent APIs, and is designed with the main goal that model implementations are self contained and easy to understand .
• Going through transformers code makes it easy for contributors to learn how an architecture works, and then port it to other frameworks such as vLLM, SGLang, MLX, llama.cpp, and many others.
• We have fully embraced this role in the ecosystem and are investing a lot of effort to make it easier.