vLLM
vLLM is a runtime maintained by PyTorch Foundation (hosted project)78 and vLLM core maintainers (project Technical Steering Committee)4. Its source code is public1 under the Apache License 2.03.
vLLM is an open-source inference and serving engine for large language models, originally built around the PagedAttention memory-management technique. It was developed in UC Berkeley's Sky Computing Lab, and UC Berkeley contributed it to the PyTorch Foundation in 2025.27
- Repository: Repository (external site: github.com)
- Documentation: Documentation (external site: docs.vllm.ai)
- License: LICENSE (external site: github.com)
- Release notes: Releases (external site: github.com)
Availability and license
Component reuse rights
- weights
- Unknown — no complete fact-level rights review
- code
- Unknown — no complete fact-level rights review
- data
- Unknown — no complete fact-level rights review
- documentation
- Unknown — no complete fact-level rights review
No complete system-rights review is recorded for this release.
Public materials checklist
| Item | Status | Notes and evidence |
|---|---|---|
| Source codeIs the source code publicly readable? | Public | 1 |
| DocumentationIs user documentation published? | Public | 5 |
| InstallationAre installation instructions or packages publicly available? | Public | Installation guides are published per hardware platform.5 |
| Supported platformsAre supported operating systems or hardware documented? | Public | Installation docs cover NVIDIA CUDA, AMD ROCm, and Intel XPU GPUs, x86, ARM AArch64, Apple silicon, and IBM Z CPUs. Other accelerators are supported through out-of-tree hardware plugins.5 |
| Release statusAre versioned releases published? | Public | Versioned releases are published on GitHub.6 |
What it is useful for
Serving language and multimodal models through an OpenAI-compatible API server, including distributed inference across multiple accelerators.2
Organization context
In the news
vLLM 0.32.0 released with a structured decisions endpoint and wider low-precision KV cache support
On October 18, 2026 the vLLM project published version 0.32.0 on GitHub. The release notes highlight a new
POST /v1/systemoneendpoint that answerschoice,noulandscorequestions over a structured state, and a newhftool and reasoning parser that reads a checkpoint'sresponse_templateso models that ship it need no model-specific parser. The notes also describe NVFP4 KV cache support on more GPU architectures and new model support, including EmbeddingGemma 2, MiniCPM-V 4.7 and Nemotron 3.5 ASR. Under security, they list fixes for three advisories and say CORS no longer allows every origin by default; breaking changes include dropping Python 3.10 and removing cascade attention. USASI has not tested any of these changes.vLLM 0.31.0 released with a preload command for faster engine restarts
On October 5, 2026 the vLLM project published version 0.31.0 on GitHub. The release notes highlight a new
vllm preloadcommand that launches a weight-cache daemon to keep post-quantized weights in GPU memory across engine restarts, and experimental engine snapshots (vllm snapshot create/restore) that use CRIU to restore a fully initialized TP1 engine. Under security, the notes say per-requestmm_processor_kwargsandmedia_io_kwargsare now rejected unless--trust-request-mm-kwargsis set. The release also lists breaking changes, including the removal oftokenizer_mode="slow", and performance work for DeepSeek-V4.1-Flash. USASI has not tested any of these changes.llm-d 0.10.0 focuses on operational hardening and deprecates its own CUDA and AWS images
The llm-d project published version 0.10.0 on GitHub on September 29, 2026. The release notes name operational hardening and production readiness as the main themes and describe a new image build arrangement: images from main and official releases go to ghcr.io and are signed publicly with cosign, while images built from open pull requests go to quay.io and are not signed. The notes deprecate the ghcr.io/llm-d/llm-d-cuda image in favor of docker.io/vllm/vllm-openai and the llm-d-aws image in favor of an AWS vLLM container, and say the llmd-fs-connector has been integrated into vLLM as the OffloadingConnector. The llm-d-workload-variant-autoscaler repository has been renamed llm-d-autoscaling. USASI has not tested this release.
U.S. eligibility
Eligible · basis: U.S.-governed project
vLLM is a PyTorch Foundation-hosted project, contributed by the University of California, Berkeley. Per the Foundation, hosted projects are governed and administered under its governance model. The PyTorch Foundation is hosted by the Linux Foundation, whose privacy policy gives a contact mailing address in San Francisco, California. vLLM's governance document says its core maintainers serve as the Technical Steering Committee under Linux Foundation project governance, and that committer status belongs to individuals, not companies.7894
Sources
This listing is not an endorsement, a safety assessment, or a federal approval.
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APA style
USASI. (2026, September 29). vLLM. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/vllm/
BibTeX
@misc{usasi_vllm,
author = {{USASI}},
title = {vLLM},
year = {2026},
month = sep,
howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/vllm/}},
note = {United States of America Superintelligence. Last updated 2026-09-29}
}The date is when this page was last updated. Add the date you read it if your style needs one.
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