vLLM
High-throughput memory-efficient LLM inference engine with PagedAttention, supporting production deployments at massive scale.
81.8K+
GitHub Stars
4.9/5
Rating
2023
Founded
Overview
vLLM is the industry-standard high-throughput inference engine for large language models, achieving state-of-the-art serving performance through innovative PagedAttention for efficient KV cache management. Developed at UC Berkeley, vLLM powers production deployments at major tech companies and AI labs, delivering up to 24x higher throughput than naive implementations while maintaining easy integration through OpenAI-compatible APIs.
The Verdict
Who Should Use vLLM?
Best For
- Production LLM serving requiring maximum throughput
- Large-scale deployments with high request volumes
- Teams needing mature, battle-tested infrastructure
- Organizations prioritizing stability and ecosystem support
Not Ideal For
- Cutting-edge experimental features (use SGLang instead)
- Extremely resource-constrained edge devices
What's Great
- High-throughput LLM inference and serving engine
- PagedAttention delivers state-of-the-art throughput
- Comprehensive model support (LLMs, vision, audio)
- Production-proven at major tech companies
- Active development and strong community
Watch Out For
- Requires significant GPU memory for optimal performance
- Setup complexity higher than managed alternatives
- Breaking changes occur between major versions
Pricing
View all features & details
Key Features
- PagedAttention for memory-efficient attention
- Continuous batching for high throughput
- Tensor and pipeline parallelism for large models
- Quantization support (AWQ, GPTQ, FP8)
- OpenAI-compatible API server
- Support for 200+ model architectures
Platforms
- NVIDIA GPUs (CUDA)
- AMD GPUs (ROCm)
- Intel GPUs
- Google TPUs
- AWS Neuron
How It Compares
| Feature | vLLM | SGLang | TGI |
|---|---|---|---|
| Maturity | Industry standard | Fast-growing | Established |
| Throughput | Excellent | Excellent | Very Good |
| Hardware Support | Broadest | NVIDIA/AMD | NVIDIA mainly |
| Best For | Production stability | Innovation | HuggingFace |
User Reviews
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