What is vLLM?
vLLM is an open-source LLM serving engine designed for high-throughput inference with efficient KV cache management, continuous batching, and OpenAI-compatible serving APIs.
How It Works
vLLM is a widely used inference engine for serving large language models efficiently. Its best-known contribution is PagedAttention, which reduces KV cache memory waste and enables higher concurrency. vLLM also provides continuous batching, model-parallel execution options, streaming responses, and API compatibility patterns that make it practical for production services and benchmarks. It is not a model itself; it is infrastructure for running supported models with better throughput and serving ergonomics.
Key Characteristics
- Open-source serving engine for LLM inference rather than a foundation model
- Uses PagedAttention-style KV cache management for memory efficiency
- Supports continuous batching for higher serving throughput
- Often exposes OpenAI-compatible API surfaces for easier integration
- Used for production deployment, benchmarking, and research systems
Common Use Cases
- Serving open-weight LLMs behind an API endpoint
- Benchmarking throughput and latency for inference workloads
- Running chat completion services with streaming responses
- Deploying models with tensor parallelism across multiple GPUs
- Testing PagedAttention and continuous batching behavior
Example
Loading code...Frequently Asked Questions
Is vLLM a language model?
No. vLLM is a serving engine used to run supported language models efficiently.
Why is vLLM associated with PagedAttention?
PagedAttention is one of vLLM's core techniques for managing KV cache memory efficiently during serving.
Does vLLM guarantee lower latency?
Not automatically. Performance depends on model, hardware, workload shape, batching, memory limits, and configuration.
When should teams consider vLLM?
It is worth considering when serving open-weight LLMs with high concurrency, streaming APIs, or throughput-sensitive workloads.