---
title: "vLLM Introduces Native-Speed Transformers Modeling Backend for Enhanced Inference Performance"
source: "Hugging Face：Blog（RSS）"
source_tier: "t1"
url: "https://huggingface.co/blog/native-speed-vllm-transformers-backend"
permalink: "https://feed.astralor.com/items/itm_f2a3364a10eb9acc95cebb72"
published: "2026-07-08T00:00:00.000Z"
score: 91
channel: "ai"
tags: ["Hugging Face","PagedAttention","Transformers","vLLM","Reasoning Acceleration"]
---

# vLLM Introduces Native-Speed Transformers Modeling Backend for Enhanced Inference Performance

## AI commentary

> This update is critical for AI engineering teams: eliminating format conversion speeds up model iteration, and 2-5x speedup directly reduces GPU costs. If you're building LLM online services or batch inference, this is practically a must-read technical update.

## At a glance

vLLM announces a native-speed Transformers modeling backend designed to significantly reduce inference latency by deeply optimizing model loading and computation pipelines. It directly integrates with Hugging Face Transformers, eliminating manual model format conversion, and supports dynamic batching and PagedAttention. Benchmarks show 2-5x throughput improvement on popular models, while maintaining ecosystem compatibility.

## Key points

- Native backend loads Hugging Face Transformers models directly without format conversion.
- Combines PagedAttention with dynamic batching for improved memory efficiency and throughput.
- Benchmarks show 2-5x inference speedup on models like LLaMA and Mistral.
- Supports continuous batching and streaming output, suitable for production deployment.

## Full text

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

---

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