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Megatron-LM
Knowledge Tutorial

Megatron-LM is a high-performance deep learning library designed by NVIDIA for training massive scale artificial intelligence models. It enables researchers to efficiently train huge text generators like GPT, image-understanding systems like LlaVA, and specialized architectures using model parallelism across thousands of GPUs.

9
Chapters
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Subsystems
∞
Rabbit Holes
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System Architecture

How the pieces fit

Megatron-LM is organized as connected concepts and components. Start broad, then drill down chapter by chapter.

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GPT (Decoder-only)
GPT (Decoder-only)
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BERT (Encoder-only)
BERT (Encoder-only)
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T5 (Encoder-Decoder)
T5 (Encoder-Decoder)
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Mamba (State Space Model)
Mamba (State Space Model)
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Mixture of Experts (MoE)
Mixture of Experts (MoE)
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RETRO
RETRO
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CLIP / SigLIP / InternViT (Vision)
CLIP / SigLIP / InternViT (Vision)
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LlaVA (Vision-Language)
LlaVA (Vision-Language)
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MIMO (Multimodal Input Multimodal Output)
MIMO (Multimodal Input Multimodal Output)
Megatron-LM — bash
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Repository Overview

Intro and Architecture Diagram

Megatron-LM is a high-performance deep learning library designed by NVIDIA for training massive scale artificial intelligence models. It enables researchers to efficiently train huge text generators like GPT, image-understanding systems like LlaVA, and specialized architectures using model parallelism across thousands of GPUs.

Source Repository: https://github.com/NVIDIA/Megatron-LM

flowchart TD A0["GPT (Decoder-only)"] A1["BERT (Encoder-only)"] A2["T5 (Encoder-Decoder)"] A3["Mamba (State Space Model)"] A4["RETRO"] A5["LlaVA (Vision-Language)"] A6["CLIP / SigLIP / InternViT (Vision)"] A7["MIMO (Multimodal Input Multimodal Output)"] A8["Mixture of Experts (MoE)"] A5 -->|"Uses as language decoder"| A0 A5 -->|"Uses as vision encoder"| A6 A7 -->|"Supports training of"| A5 A0 -->|"Integrates for sparsity"| A8 A4 -->|"Enhances with retrieval"| A0 A3 -->|"Can integrate"| A8 A2 -->|"Encoder architecturally res..."| A1
Tutorial Chapters

All 9 chapters

Follow sequentially or jump to any topic. Start with GPT (Decoder-only).

Ch.01 CORE
GPT (Decoder-only)
Welcome to the world of Large Language Models (LLMs)! If you've ever used ChatGPT, Claude, or LLaMA, you have interacted with a GPT (Genera…
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Ch.02 CORE
BERT (Encoder-only)
In the previous chapter, GPT (Decoder-only), we learned about models that act like writers, generating text one word at a time by looking b…
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Ch.03 CORE
T5 (Encoder-Decoder)
In the previous chapters, we looked at two distinct specialists:
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Ch.04 CORE
Mamba (State Space Model)
In the previous chapters, we explored the "Transformer" family:
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Ch.05 CORE
Mixture of Experts (MoE)
In the previous chapter, Mamba (State Space Model), we learned how to process incredibly long books by changing how the model remembers his…
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Ch.06 CORE
RETRO
In the previous chapter, Mixture of Experts (MoE), we learned how to make models smarter and faster by creating "specialists" inside the br…
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Ch.07 CORE
CLIP / SigLIP / InternViT (Vision)
In the previous chapter, RETRO, we gave our model an "Open Book" so it could look up external text information. We expanded its memory.
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Ch.08 CORE
LlaVA (Vision-Language)
In the previous chapter, CLIP / SigLIP / InternViT (Vision), we built the Eyes of our AI. We learned how to turn pixels into mathematical v…
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Ch.09 CORE
MIMO (Multimodal Input Multimodal Output)
In the previous chapter, LlaVA (Vision-Language), we gave our AI a pair of Eyes. We connected a Vision Encoder (CLIP) to a Language Brain (…
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About This Project

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