vLLM
Researched 18d agoInference RuntimeTier 3vllm-project
vLLM is a self-hosted runtime, not a hosted model catalog. You bring compatible open-weight models, so there is no per-token pricing to track.
Covers 0 workload areas across 0 tracked models; last verified 2026-06-29.
Use it for
- Getting oriented before committing to a specific model
Do not use it for
- Final benchmark picks without opening the relevant model detail page
Tracked models
0
Models available through this provider
Priced output routes
0
Output pricing not yet tracked
Cheapest output
Unknown
Output pricing not yet tracked
Batch-ready models
0
No batch pricing tracked
Latest model release
Unknown
Release date of the newest tracked model
Freshness
2026-06-29
Researched 18d ago
Information
vLLM is a high-throughput, memory-efficient open-source inference engine and serving library for large language models. It is self-hosted rather than a managed token-priced API, and it ships an OpenAI-compatible HTTP server for local or private deployments. The vllm-project also maintains vLLM-Omni, a sibling runtime for serving omnimodal workloads.
Catalog freshness
No confirmed release dates yet for the models tracked on this provider.
Where this host wins
Not enough capability or benchmark coverage yet to call strengths for this provider.
Getting started
Official product, docs, and pricing links — confirm quotas and regions in the vendor docs.
SDKs & libraries
Compliance notes
No verified compliance claims (SOC 2, ISO, HIPAA) tracked for this provider yet — check the vendor's trust center for current certifications.
Platform Overview
vLLM does not offer a fixed hosted model catalog. Operators bring their own open-weight Hugging Face model weights, run vLLM on their own compute, and serve compatible architectures through a local or private OpenAI-compatible endpoint. vLLM-Omni (github.com/vllm-project/vllm-omni) follows the same runtime pattern for omnimodal models across text, image, audio, video, and robotics inputs or outputs; the models it serves remain separate model-family and researcher decisions. LLMReference intentionally does not attach token-priced modelProvider rows to vLLM because the runtime has no per-model commercial offer or published token pricing; users pay their own GPU or infrastructure costs.
Compare per-model pricing, input and output token costs, batch availability, and benchmark coverage.