SoftwareRuntime

MLC LLM

MLC LLM is a runtime maintained by MLC team (MLC open community)1 6 and CMU Catalyst (lists MLC LLM among its own research projects)8. Its source code is public1 2 under the Apache License 2.02.

MLC LLM is an open-source machine learning compiler and deployment engine for large language models. Compiled models run on MLCEngine, which offers an OpenAI-compatible API through a REST server and Python, JavaScript, iOS, and Android interfaces, with GPU backends including CUDA, ROCm, Vulkan, Metal, WebGPU, and OpenCL.1

Repository: GitHub repository (external site: github.com)
Documentation: Documentation (external site: llm.mlc.ai)
Documentation: Installation (external site: llm.mlc.ai)
License: License (Apache 2.0) (external site: raw.githubusercontent.com)

Last reviewedSep 29, 2026Entry updated Oct 1, 2026Documented release Unknown

Availability and license

Overall availability

Public

Source code on GitHub under the Apache License 2.0. The installation guide distributes nightly pre-release pip wheels, recommends installing them into a conda environment, and also documents building from source.1 2 4

Availability fact review: not separately recorded

Availability is separate from permission: read the license before using or redistributing.

Apache License 2.0 (external site: raw.githubusercontent.com)2
Applies to: Code · SPDX Apache-2.0 · on the rubric’s OSI-approved software-license list · fact-level rights review not recorded
Plain-language guide to the Apache License 2.0

Component reuse rights

A readable or downloadable component is not automatically reusable. These indicators concern recorded license evidence, not system certification.

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

Items for a runtime under USASI rubric v0.2. Unknown means unassessed or insufficient evidence.

Public materials checklist for MLC LLM
Item	Status	Notes and evidence
Source codeIs the source code publicly readable?	Public	Published on GitHub under the Apache License 2.0.1 2
DocumentationIs user documentation published?	Public	Documentation covers installation, a quick start, and deployment to the web, iOS, Android, REST, Python, and the command line.3
InstallationAre installation instructions or packages publicly available?	Public	Nightly-built pip wheels from the MLC wheel index or building from source; the guide recommends installing into a conda environment.4
Supported platformsAre supported operating systems or hardware documented?	Public	The README gives a support matrix covering Linux, Windows, macOS, web browsers (WebGPU and WASM), iOS/iPadOS, and Android, with AMD, NVIDIA, Apple, Intel, Adreno, and Mali GPUs.1 4
Release statusAre versioned releases published?	Partial	The installation guide distributes nightly pre-release builds. The repository's tags include v0.20.0 (July 7, 2026), whose commit message describes it as a stable release; the installation guide does not document a packaged stable release.4 5

What it is useful for

Deploying language models natively on desktop and server GPUs, mobile devices, and in web browsers from a shared engine and compiler.1

Organization context

Carnegie Mellon University

People: Tianqi Chen

U.S. eligibility

Project eligibility rests on documented governing or maintaining entities, not on contributors.

Eligible · basis: U.S.-governed project

Carnegie Mellon University's Catalyst research group lists MLC LLM as one of its own research projects, which documents a U.S. university lab as a maintaining entity. The project credits the "MLC team" and describes MLC as an open community; every organization the MLC site lists as supporting and contributing (NSF, CMU, Catalyst, Purdue, NVIDIA, Amazon, and Google) is U.S.-based. MLC's 2023 announcement says the project was started by members of CMU Catalyst, UW SAMPL, Shanghai Jiao Tong University, OctoML, and the MLC community; that multi-institution origin is recorded here. Eligibility rests on the documented CMU lab, not on contributors' affiliations or nationality.8 6 1 7

Assessed Oct 1, 2026

Sources

1.
MLC LLM README (external site: raw.githubusercontent.com)
MLC community · Repository · accessed Sep 29, 2026 · evidence reviewed Oct 1, 2026
2.
MLC LLM LICENSE (external site: raw.githubusercontent.com)
MLC community · License · accessed Sep 29, 2026
3.
MLC LLM documentation (external site: llm.mlc.ai)
MLC community · Documentation · accessed Sep 29, 2026
4.
Install MLC LLM Python Package (external site: llm.mlc.ai)
MLC community · Documentation · accessed Sep 29, 2026
5.
Tags: mlc-ai/mlc-llm (external site: github.com)
MLC community · Release notes · accessed Sep 29, 2026
6.
MLC - A Community of Machine Learning Compilers (external site: mlc.ai)
MLC community · Official page · accessed Sep 29, 2026 · evidence reviewed Oct 1, 2026
7.
Bringing Hardware Accelerated Language Models to Android Devices (external site: blog.mlc.ai)
MLC community · Announcement · published May 8, 2023 · accessed Sep 29, 2026
8.
CMU Catalyst (external site: catalyst.cs.cmu.edu)
Carnegie Mellon University (Catalyst group) · Official page · accessed Sep 29, 2026 · evidence reviewed Oct 1, 2026

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This listing is not an endorsement, a safety assessment, or a federal approval.

Cite this page

APA style

USASI. (2026, October 1). MLC LLM. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/mlc-llm/

BibTeX

@misc{usasi_mlc_llm,
  author = {{USASI}},
  title = {MLC LLM},
  year = {2026},
  month = oct,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/mlc-llm/}},
  note = {United States of America Superintelligence. Last updated 2026-10-01}
}

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