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Decision brief
Megatron-LM from NVIDIA is a research-focused tool for developing and training large-scale language models with transformer architectures, emphasizing efficient parallelism across multiple GPUs.
Good fit when
- The tool is particularly beneficial when your project is GPU-centric and benefits from advanced parallelism techniques such as Tensor, Pipeline, Data, Expert, and Cluster Parallelisms (TP, PP, DP, EP,
- and CP) provided by NVIDIA's ecosystem.
Avoid when
- Avoid Megatron-LM if your computational setup does not include NVIDIA GPUs as it leverages GPU-specific features and parallelisms that may not be available or efficient on non-NVIDIA hardware.
- If you need portability across various hardware without depending on proprietary optimizations, other tools might better serve your needs.
- Requirements:
- Min 32 GB RAM; Requires NVIDIA GPUs for optimized performance. Non-GPU usage is not supported or recommended.; Installation from source can be resource-intensive and may require limiting parallel compilation jobs to avoid running out of memory.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 1w
- Provenance
- Not a fork · Organization account
- As of 1w
- Security (OSV)
- No lockfile
- As of 1mo
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Backing
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- Company
- NVIDIA Corporation·GitHub org profile·1mo
- Employees
- 11,528·Wikidata (P1128 employees)·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
pip install Megatron-LMHow it fits your stack(9)
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Evidence and technical details
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Overview
Megatron-LM is a repository from NVIDIA focused on the development and training of large-scale language models using transformer architectures. It provides tools for efficient parallelism strategies across multiple GPUs.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 7, 2026
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 7, 2026)
uv pip install megatron-coreSource link
Tags
README
Getting Started
Install from PyPI:
uv pip install megatron-core
Or clone and install from source:
git clone https://github.com/NVIDIA/Megatron-LM.git
cd Megatron-LM
uv pip install -e .
Note: Building from source can use a lot of memory. If the build runs out of memory, limit parallel compilation jobs by setting
MAX_JOBS(for example,MAX_JOBS=4 uv pip install -e .).
For NVIDIA GPU Cloud (NGC) container setup and all installation options, review the Installation Guide.
- Your First Training Run - End-to-end training examples with data preparation
- Parallelism Strategies - Scale training across GPUs with TP, PP, DP, EP, and CP
- Contribution Guide - How to contribute to Megatron Core
For agents
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