Tool · Inference runtime · vLLM Project
vLLM
A high-throughput and memory-efficient inference and serving engine for LLMs
- License
- PermissiveApache License 2.0
- Language
- Python
- Latest release
- 2 Nov 2026vLLM v0.33.0
- Measurements
- 1with this runtime
Relationships
- ToolLiteLLMIntegrated by
Releases & news
- vLLM v0.33.0This release features 927 commits from 389 contributors (122 new)!
- vLLM v0.33.0vLLM v0.33.0
- vLLM v0.32.0v0.32.0 Highlights This release features 814 commits from 363 contributors (121 new)! Structured decisions and generic parsers : the new POST /v1/systemone endpoint answers choice , noul and score questions over a structured state ( #59299 ), is served by default ( #60677 ,…
- vLLM proto-v0.6.0vllm-proto 0.6.0
- vLLM proto-v0.5.0Release vllm-proto 0.5.0
- vLLM v0.31.0This release features 717 commits from 307 contributors (96 new)!
- vLLM proto-v0.4.0Release vllm-proto 0.4.0
- vLLM v0.30.0v0.30.0 Highlights This release features 762 commits from 315 contributors (104 new)! New models : DeepSeek-V4.1-Flash ( #56214 , #56228 , #56208 ) with the whole KV stored in MXFP8 through the FlashMLA V4.1 record on SM100 ( #56893 ), DeepGEMM Mega-mHC ( #56962 ), and async…
- vLLM proto-v0.3.0Release vllm-proto 0.3.0
- vLLM proto-v0.2.0Validated by PR #56538 CI at fa2a26f .
- proto-v0.1.0vllm-proto 0.1.0
- vLLM v0.29.0This release features 594 commits from 277 contributors (91 new)!
- vLLM v0.28.0This release features 584 commits from 270 contributors (76 new)!
- vLLM v0.27.1This is a patch release on top of v0.27.0.
- vLLM v0.27.0This release features 561 commits from 242 contributors (64 new)!
- vLLM v0.26.0This release features 411 commits from 212 contributors (61 new)!
- vLLM v0.25.1This release features 2 commits from 2 contributors (1 new)!
- vLLM v0.25.0This release features 558 commits from 232 contributors (64 new)!
- vLLM v0.24.0This release features 571 commits from 256 contributors (77 new)!
- vLLM v0.23.0Please note that Minimax M3 is not yet supported in this version. Please follow vLLM recipe for usage guides for M3.
Measured performance with this runtime
Throughput members and projects recorded, in tokens per secondHow fast the model writes its answer; about 10 tokens per second reads like comfortable typing..
| Model · quantization | System | Runtime | Context | Measurements | Source |
|---|---|---|---|---|---|
| Qwen2.5 32B Instruct AWQ 4-bit | A100 80GB server (512 GB DDR4) | vLLMcuda · 0.7.3 | 8K | 48 tok/s · Generation throughput 72 GB · Peak memory 5,200 tok/s · Prompt throughput 95 ms · Time to first token | Mutinai illustrative fixtures |
Reviews
Contributions are not open yet, so there is nothing here from members.
Sources & history
Sources
- GitHub (live source, 6 records, 2 Nov 2026)
- GitHub (fixture) (illustrative fixture, 1 record, 13 Sept 2026)
- Mutinai illustrative fixtures (illustrative fixture, 1 record, 13 Sept 2026)
External identifiers
- github vllm-project/vllm
Field history
- homepageUrl = "https://vllm.ai" — GitHub (current)
- homepageUrl = "https://vllm.ai" — GitHub
- homepageUrl = "https://vllm.ai" — GitHub
- homepageUrl = "https://vllm.ai" — GitHub
- homepageUrl = "https://vllm.ai" — GitHub
- homepageUrl = "https://vllm.ai" — GitHub
- homepageUrl = "https://docs.vllm.ai" — GitHub (fixture)
- primaryLanguage = "Python" — GitHub (current)
- primaryLanguage = "Python" — GitHub
- primaryLanguage = "Python" — GitHub
- primaryLanguage = "Python" — GitHub
- primaryLanguage = "Python" — GitHub