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vLLM Review 2026 — The Best Open-Source LLM Serving Engine
vLLM is the production standard for serving open-source LLMs. PagedAttention delivers 10-24x throughput vs naive inference. OpenAI-compatible API, 50+ models, free Apache 2.0.
vLLM runs open-source language, multimodal, embedding, retrieval, reward, and classification models for inference and production serving. It provides an OpenAI-compatible API, plus Anthropic Messages API and gRPC support.
It is used by developers, research teams, and infrastructure operators deploying models on GPUs, CPUs, and other accelerators. vLLM is software rather than a hosted model service; operators provide the hardware and pay any related infrastructure costs.
Features
- Continuous batching, chunked prefill, and prefix caching
- PagedAttention for key-value memory management
- OpenAI-compatible API server, plus Anthropic Messages API and gRPC support
- Tensor, pipeline, data, expert, and context parallelism
- Quantization with FP8, INT8, INT4, GPTQ/AWQ, GGUF, and related formats
- Speculative decoding with n-gram, suffix, EAGLE, and DFlash
- Support for NVIDIA, AMD, Intel GPUs and x86, ARM, and PowerPC CPUs
- Apache-2.0 license
Use cases
- Serve open-source language models behind an application API
- Run high-throughput batch inference for production workloads
- Deploy multimodal, embedding, retrieval, reward, or classification models
- Reduce GPU memory use with quantization and prefix caching
- Distribute inference across multiple accelerators
- Integrate model serving with OpenAI-compatible clients