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distributed-llm-pretraining-torchtitan

Distribute LLM pretraining across GPUs with PyTorch and torchtitan.

Updated Jun 26, 2026
Search Tags:#PyTorch#distributed training#LLM pretraining#scale-up#4D parallelism
One-click install
npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill distributed-llm-pretraining-torchtitan-nitish-gitbit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: distributed-llm-pretraining-torchtitan
Source: https://github.com/NITISH-gitbit/hermes-custom/tree/main/optional-skills/mlops/torchtitan
Command: npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill distributed-llm-pretraining-torchtitan-nitish-gitbit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch>=2.6.0, torchtitan>=0.2.0, torchao>=0.5.0, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of pretraining large language models (LLMs) at scale, providing a PyTorch-native solution for distributed LLM pretraining with 4D parallelism.

Core Features & Use Cases

  • Distributed Pretraining: Supports pretraining LLMs from 8 to 512+ GPUs with various models and frameworks.
  • 4D Parallelism: Implements full support for FSDP2, TP, PP, and CP for optimal performance.
  • Float8 and torch.compile: Enhances performance with advanced numerical methods and compilation techniques.
  • Use Case: Ideal for scaling LLM pretraining with models like Llama 3.1, DeepSeek V3, and custom models on H100 GPUs.

Quick Start

Use the distributed-llm-pretraining-torchtitan skill to pretrain Llama 3.1 8B on 8 GPUs.

Frequently Asked Questions about distributed-llm-pretraining-torchtitan

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run distributed LLM pretraining with PyTorch across multiple GPUs?▼

Distributed LLM pretraining with PyTorch uses torchtitan to distribute and optimize model training across multiple GPUs. It targets large-scale scenarios by implementing 4D parallelism to handle workloads from 8 to 512+ GPUs.

What is 4D parallelism and how does it help with LLM pretraining?▼

4D parallelism for LLM pretraining combines FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism. This PyTorch-native approach optimizes performance and memory distribution when scaling large model training across multiple GPUs.

Do I need specific PyTorch versions to use torchtitan for scale-up training?▼

Yes, torchtitan for scale-up training requires PyTorch version 2.6.0 or higher, torchtitan 0.2.0 or higher, and torchao 0.5.0 or higher. These dependencies provide the necessary compilation and numerical methods for execution.

Can I use this approach to pretrain Llama 3.1 models on H100 GPUs?▼

Yes, you can pretrain Llama 3.1 models on H100 GPUs using this PyTorch-native approach. It is specifically designed to scale LLM pretraining for models like Llama 3.1 8B and DeepSeek V3 starting from 8 GPUs.

What's the best way to accelerate large language model pretraining with float8?▼

To accelerate LLM pretraining with float8, use this PyTorch-native approach which integrates advanced numerical methods like float8 and torch.compile. This enhances performance during large-scale distributed training across multiple GPUs.

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