vLLM
Researched 108d 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 108d 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.
Where this host wins
Not enough capability or benchmark coverage yet to call strengths for this provider.
Getting started
SDKs & libraries
Platform Overview
vLLM is open-source inference and serving software rather than a hosted API, so it has no model catalog or per-token price: you install it, load open models from Hugging Face, and run them on your own GPUs, CPUs, or other accelerators, paying for that hardware or cloud compute yourself. It exposes an OpenAI-compatible API server for local or private deployments.