vLLM

Most LLM inference setups treat GPU memory as a flat buffer and batch requests in fixed windows — both choices leave performance on the table the moment traffic gets uneven. vLLM addresses this at the engine level. It manages the key-value cache through PagedAttention, a memory allocation scheme the project describes as analogous to OS virtual memory paging, which reduces fragmentation and lets more concurrent requests fit on the same hardware. Continuous batching means the engine processes new requests as soon as a slot frees, rather than waiting for a full batch — so GPU utilization stays high even under irregular traffic patterns.

The differentiating feature for teams migrating off OpenAI is the drop-in API compatibility. vLLM exposes an OpenAI-format endpoint, so any client already calling the OpenAI API can point at a self-hosted vLLM instance with a URL change. The docs describe support for chat completions, completions, and embeddings endpoints. This matters in practice because it means the integration cost for swapping providers is close to zero — no SDK changes, no prompt reformatting.

vLLM fits tightest in single-node or small-cluster GPU deployments where the goal is maximum throughput from models in the DeepSeek, Llama, Qwen, Mistral, and Gemma families — all of which appear on the vendor’s supported model list. It breaks down when the deployment requirement is heterogeneous hardware at scale: the CUDA path receives the most active development, and teams running AMD ROCm or Huawei Ascend NPUs report a narrower set of validated configurations. Teams that need managed inference without operating their own GPU infrastructure will need a different solution entirely — vLLM is infrastructure, not a service.

Installation targets Python 3.10 or higher (3.12+ recommended by the docs), and the project supports CUDA, ROCm, XPU, and CPU targets through a unified install path. Docker images are provided alongside the Python package. The ecosystem page lists purpose-built companions: LLM Compressor for quantization, GuideLLM for performance evaluation, and Production Stack for Kubernetes deployment — each a separate project maintained alongside vLLM.