vLLM is an LLM inference and serving library focused on high-throughput generation. It belongs in the model execution and serving layer, where scheduling, batching, cache handling, and provider-facing APIs dominate the design.
At a glance
- Organization
- vLLM project
- Runtime role
- High-throughput LLM serving
- Category
- Inference and Serving
- Official documentation
- Visit official documentation opens in a new tab
Where it fits in the runtime stack
Layer 3 and Layer 4: LLM inference engine and serving runtime.
Primary runtime role
Use vLLM when a runtime needs efficient LLM serving, online inference APIs, batching, metrics, and production serving patterns.
Not the same as
vLLM is not a complete agent runtime by itself; it needs surrounding controls for tools, memory, identity, and governance.
Integration notes
- Place vLLM behind explicit routing policy and admission control.
- Record TTFT, TPOT, queue time, token counts, and cache behavior in runtime telemetry.
- Treat OpenAI-compatible serving endpoints as model execution interfaces, not product workflow boundaries.
Questions before production use
- What concurrency, context length, latency, and throughput targets does the runtime need?
- How will model loading, autoscaling, and graceful draining be handled?
- What trace fields connect vLLM requests to upstream runtime decisions?
Review and deprecation posture
This profile is reviewed as part of the aRuntime.com quarterly resource audit. If the official documentation moves, the project is archived, or the resource changes scope, this page should be updated with a dated status note rather than silently removed.
Sources and further reading
- vLLM documentation opens in a new tab — vLLM project; official documentation; accessed 2026-06-20 UTC.
Last reviewed: .