Vllm Project · 2× · vu le 25 août 2026
35
Momentum
vLLM is an Open Source library for efficient Inference and serving of Large Language Models, originally developed at UC Berkeley's Sky Computing Lab. Its core is the PagedAttention algorithm, which organizes KV-Cache memory in blocks analogous to virtual memory management, drastically reducing memory waste. vLLM supports hundreds of model architectures from Hugging Face, offers an OpenAI-compatible HTTP server, and runs on diverse hardware (NVIDIA, AMD, Intel GPUs, TPUs, AWS Trainium/Inferentia, and others). The project is licensed under Apache 2.0 and is being further developed by a broad community from academia and industry.
Historique du momentum
27.05.25.08.
Fonctionnalités
| Deployment (Self-Hosted/Cloud) | Self-hosted via Docker/Kubernetes (Helm charts, 'Production Stack'); deployable on cloud platforms such as Google Cloud (GKE, Compute Engine, Vertex AI) |
| Throughput/Latency | Up to 24x higher throughput than HuggingFace Transformers and 2.2x-3.5x higher than HuggingFace TGI (depending on scenario) |
| License | Apache License 2.0 |
| Platform | Python library/server; runs on NVIDIA, AMD, Intel GPUs/CPUs, TPU, PowerPC, AWS Trainium/Inferentia |
| Price | Free, open source (self-hosted); costs only for own infrastructure/cloud compute |
| Protocol Compatibility | HTTP server compatible with OpenAI Completions, Chat, and Embeddings API; endpoints include /v1/chat/completions, /v1/completions, /v1/embeddings |
| Release Date | Initial release June 2023; ongoing active development with frequent version releases (e.g. v0.14.1) |
| Supported Models/Providers | 200+ model architectures via Hugging Face, incl. Llama, Qwen, GPT-OSS, LLaVA (multimodal) |