vLLM
Open-sourceHigh-throughput and memory-efficient inference and serving engine for production LLM deployments.
About vLLM
vLLM is a high-throughput and memory-efficient inference and serving engine for Large Language Models. Written in Python, it achieves state-of-the-art serving throughput through PagedAttention and continuous batching, with 77.8k GitHub stars.
Best For
- Production LLM deployments needing high throughput
- Organizations serving LLMs to many concurrent users
Pros & Cons
Pros
- + Industry-leading throughput for LLM serving workloads
- + Memory-efficient implementation reduces hardware costs
- + OpenAI-compatible API simplifies migration from cloud providers
Cons
- - Requires GPU hardware for practical performance
- - Configuration and tuning require deep technical knowledge
Pricing
Open source and free to use
Key Features
- State-of-the-art serving throughput with PagedAttention
- Continuous batching for efficient request handling
- Support for all major LLM architectures and quantization methods
- OpenAI-compatible API server for easy integration
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