What It Does
vLLM is an open-source library for fast LLM inference and serving. It implements PagedAttention for efficient memory management, enabling high-throughput serving of large language models on standard GPU hardware.
Key Features
- PagedAttention — efficient KV-cache memory management for higher throughput
- Continuous batching — process multiple requests simultaneously
- Tensor parallelism — distribute models across multiple GPUs
- OpenAI-compatible API — drop-in replacement for OpenAI API endpoints
- Quantization support — AWQ, GPTQ, FP8 for reduced memory usage
- LoRA serving — serve multiple fine-tuned adapters efficiently
- Speculative decoding — faster generation through draft models
Pricing Breakdown
| Component | Cost |
|---|---|
| vLLM | Free (Apache 2.0) |
| Hardware | User provides GPUs |
Who It’s For
Teams self-hosting LLMs who want maximum throughput per GPU dollar, infrastructure engineers building LLM serving platforms, and companies needing full control over their inference stack.
Competitive Position
vLLM is the de facto standard for self-hosted LLM serving, used by most inference providers under the hood (including Together AI and others). PagedAttention was a breakthrough in serving efficiency. Competes with TensorRT-LLM (NVIDIA, more complex) and SGLang (newer, focuses on programming model). The open-source community ensures rapid feature development and broad model support.