來源Hugging Face Blog•較早收集於 16h
Hugging Face Transformers 整合 vLLM 原生速度後端
#inference#high-throughput#deployment#optimizationvllmhugging facevllmtransformers
💡透過 Hugging Face Transformers 直接使用全新的原生 vLLM 後端，大幅提升您的 LLM 推論吞吐量。
⚡ 30 秒速覽
有什麼變化
在 Transformers 函式庫中實現原生 vLLM 整合
為什麼重要
此更新縮小了研究導向模型載入與生產級推論速度之間的差距。團隊無需脫離標準的 Hugging Face 工作流程，即可擴展其 LLM 應用程式。
下一步行動
更新您的 Transformers 函式庫，並使用現有模型測試新的 vLLM 後端，以評估吞吐量的提升。
誰應關注：Developers & AI Engineers
關鍵要點
- •在 Transformers 函式庫中實現原生 vLLM 整合
- •顯著提升支援模型的推論吞吐量
- •簡化高效能 LLM 服務的部署流程
🧠 深度解析
本篇為 AI 生成分析，非原文內容。
🔑 增強重點摘要
- •The integration utilizes PagedAttention, a memory management algorithm that significantly reduces KV cache memory waste, allowing for higher batch sizes.
- •It supports continuous batching, which dynamically batches incoming requests to maximize GPU utilization without waiting for the entire batch to complete.
- •The backend is designed to be compatible with the existing Hugging Face
AutoModelAPI, minimizing code changes for developers transitioning from standard Transformers inference. - •It includes native support for Tensor Parallelism, enabling the distribution of large models across multiple GPUs for reduced latency.
- •The integration leverages vLLM's optimized CUDA kernels, which are specifically tuned for common LLM architectures like Llama, Mistral, and Qwen.
📊 競品分析▸ Show
| Feature | vLLM (HF Integration) | NVIDIA TensorRT-LLM | TGI (Text Generation Inference) |
|---|---|---|---|
| Ease of Use | High (Native HF) | Moderate | High |
| Throughput | Very High | Very High | High |
| Architecture | PagedAttention | TensorRT Optimization | Continuous Batching |
| Pricing | Open Source | Open Source | Open Source |
🛠️ 技術深入
- Implements PagedAttention to manage KV cache in non-contiguous memory, effectively eliminating fragmentation.
- Utilizes a custom request scheduler that supports continuous batching, allowing new requests to be added to a batch as soon as others finish.
- Integrates with the Hugging Face
transformerslibrary via a specializedvLLMbackend class that intercepts forward passes. - Supports FP8 and INT8 quantization natively to reduce memory footprint and increase inference speed on supported hardware.
- Leverages custom CUDA kernels for operations like FlashAttention-2 and PagedAttention to minimize overhead.
🔮 前景展望基於引用來源的 AI 分析
Standard Transformers inference will become deprecated for production workloads.
The performance gap between standard Transformers and vLLM-backed inference is too large for high-traffic production environments to ignore.
Hugging Face will unify its inference stack around vLLM.
By integrating vLLM natively, Hugging Face is signaling a shift away from maintaining fragmented inference backends toward a single, high-performance standard.
⏳ 時間線
2023-06
vLLM is open-sourced by researchers at UC Berkeley, introducing PagedAttention.
2023-11
Hugging Face begins deepening collaboration with vLLM for TGI (Text Generation Inference) compatibility.
2024-05
vLLM reaches v0.4.0, significantly expanding support for multi-modal models and new architectures.
2025-02
Hugging Face announces the initiative to integrate vLLM directly into the Transformers library.
2026-07
Native-speed vLLM integration is officially released within the Transformers ecosystem.
📰
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原始來源： Hugging Face Blog ↗
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