SoftwareResearch stack

Megatron-LM and Megatron Core

Megatron-LM and Megatron Core is a research stack maintained by NVIDIA2 5. Its source code is public1 under the BSD 3-Clause License (NVIDIA code)2.

NVIDIA's Megatron-LM repository contains two components: Megatron Core, a library of GPU-optimized building blocks for training transformer models at scale (tensor, pipeline, data, expert, and context parallelism; FP16, BF16, FP8, and FP4 mixed precision), and Megatron-LM, a reference training setup with pre-configured scripts built on Megatron Core. The README says Megatron Core development moved to GitHub in December 2025, with all development and CI now happening in the open.1

Last reviewedSep 29, 2026Entry updated Sep 29, 2026Documented release Unknown

Availability and license

Overall availability

Public

Documented as available to the general public. Access conditions and license terms may still apply.1 2

Availability fact review: not separately recorded

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

BSD 3-Clause License (NVIDIA code) (external site: raw.githubusercontent.com)2
Applies to: Code · SPDX BSD-3-Clause · on the rubric’s OSI-approved software-license list · fact-level rights review not recorded
Plain-language guide to the BSD 3-Clause License

The LICENSE file applies BSD 3-Clause terms (copyright NVIDIA Corporation) to all files unless otherwise noted, and states that the repository also contains third-party code under the Apache License 2.0 and MIT License, identified by file headers. The README badge reads "Apache", and the megatron-core PyPI metadata gives "Apache 2.0" in its license field while listing a BSD license classifier; this record follows the LICENSE file.2 1 5

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 research stack under USASI rubric v0.2. Unknown means unassessed or insufficient evidence.

Public materials checklist for Megatron-LM and Megatron Core
Item	Status	Notes and evidence
Source codeIs the source code publicly readable?	Public	Public GitHub repository; the README says all development and CI now happen in the open.1
DocumentationIs user documentation published?	Public	The Megatron Core developer guide covers installation, a first training run, data preparation, and parallelism.3 4
Training codeDoes it include code for training models?	Public	Includes training scripts and examples, such as a Llama 3 8B FP8 training script, plus post-training (quantization, distillation, pruning) and reinforcement learning code.1 4
Data informationAre the data it expects or ships with documented?	Public	The quickstart documents the expected JSONL input and the preprocessing script that tokenizes it into binary .bin/.idx files.4
ReproducibilityAre instructions for reproducing reported results published?	Unknown	The README describes its scaling benchmark configurations and points to the Megatron Bridge performance summary; whether published instructions reproduce those results was not assessed.1

What it is useful for

The README presents Megatron-LM for research teams, learning distributed training, and experimentation, and Megatron Core for developers building custom training frameworks. NVIDIA's Megatron Bridge library builds on Megatron Core for Hugging Face checkpoint conversion and training recipes.1

Run and use notes

Documented facts only. No hardware or performance claims are made without a cited source and stated assumptions.

The installation guide lists PyPI (megatron-core), source, and NGC PyTorch container installs; it recommends NVIDIA Turing or later GPUs, requires Hopper, Ada, or Blackwell GPUs for FP8, and lists PyTorch 2.6 or later. The guide lists Python 3.10 or later (3.12 recommended), while the README says the 0.17.0 release drops Python 3.10 support and the current PyPI package (0.19.2) requires Python 3.12 or later.3 1 5

Organization context

NVIDIA

What this catalog does not know

Unknown means the sources reviewed for this record do not document it. It is not evidence that something does not exist.

Reproducibility: unknown.

Have a primary source? How to report a correction.

U.S. eligibility

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

Eligible · basis: U.S.-governed project

The repository is published under NVIDIA's GitHub organization, its license names NVIDIA Corporation as copyright holder for the project's own code, and the megatron-core PyPI package lists NVIDIA as author and maintainer. NVIDIA's principal executive offices are in Santa Clara, California, per its Form 10-Q for the quarter ended July 26, 2026.2 5 6

Assessed Sep 29, 2026

Sources

1.
NVIDIA/Megatron-LM README (external site: raw.githubusercontent.com)
NVIDIA (GitHub) · Repository · accessed Sep 29, 2026
2.
NVIDIA/Megatron-LM LICENSE (external site: raw.githubusercontent.com)
NVIDIA (GitHub) · License · accessed Sep 29, 2026
3.
Installation — Megatron Core developer guide (external site: docs.nvidia.com)
NVIDIA · Documentation · accessed Sep 29, 2026
4.
Your First Training Run — Megatron Core developer guide (external site: docs.nvidia.com)
NVIDIA · Documentation · accessed Sep 29, 2026
5.
megatron-core on PyPI (JSON metadata) (external site: pypi.org)
Python Package Index · Release notes · accessed Sep 29, 2026
6.
NVIDIA Corporation Form 10-Q for the quarter ended July 26, 2026 (external site: sec.gov)
U.S. Securities and Exchange Commission (EDGAR) · Filing · accessed Sep 29, 2026

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Cite this page

APA style

USASI. (2026, September 29). Megatron-LM and Megatron Core. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/megatron-lm/

BibTeX

@misc{usasi_megatron_lm,
  author = {{USASI}},
  title = {Megatron-LM and Megatron Core},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/megatron-lm/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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