distributed-llm-pretraining-torchtitan

Enable PyTorch-native distributed LLM pretraining with TorchTitan's 4D parallelism across 8 to 512+ GPUs.

2|Updated Feb 4, 2026

Search Tags:#llm #pytorch #distributed training #pretraining #torchtitan #4d parallelism

Ready to run

npx skills add https://github.com/zhuangbiaowei/smart_bot --skill distributed-llm-pretraining-torchtitan-zhuangbiaowei

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: distributed-llm-pretraining-torchtitan
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/torchtitan
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill distributed-llm-pretraining-torchtitan-zhuangbiaowei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of efficiently pretraining large language models (LLMs) at scale, from 8 to 512+ GPUs, by providing a PyTorch-native framework with advanced parallelism techniques.

Core Features & Use Cases

4D Parallelism: Supports composable parallelism including FSDP2, Tensor Parallelism (TP), Pipeline Parallelism (PP), and Context Parallelism (CP).
Optimized for Scale: Designed for training massive models like Llama 3.1 and DeepSeek V3.
Performance Enhancements: Integrates Float8 precision, torch.compile, and distributed checkpointing for speed and efficiency.
Use Case: Pretraining a 70B parameter model on a cluster of 256 GPUs using a combination of FSDP, TP, and PP to achieve optimal training throughput and memory utilization.

Quick Start

Use the torchtitan skill to pretrain Llama 3.1 8B on 8 GPUs by running the command CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh.

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 pretrain a large language model like Llama 3.1 on multiple GPUs using PyTorch?▼

To pretrain LLMs like Llama 3.1 on multiple GPUs, you can use this PyTorch-native framework to run distributed training across 8 to 512+ GPUs with 4D parallelism. It provides the necessary scripts to initiate the process efficiently.

What is 4D parallelism in distributed LLM training and when do I need it?▼

4D parallelism in distributed LLM training composes FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism to maximize throughput. You need it when pretraining massive models that exceed the memory of a single GPU cluster.

Does PyTorch distributed training support Float8 precision and torch.compile for large models?▼

Yes, PyTorch distributed training supports Float8 precision and torch.compile for large models. This framework integrates these features alongside distributed checkpointing to enhance speed and overall training efficiency.

Can I use TorchTitan for pretraining DeepSeek V3 models on a 256 GPU cluster?▼

Yes, you can use TorchTitan for pretraining DeepSeek V3 models on a 256 GPU cluster. It is specifically optimized to scale training for massive models by combining FSDP, TP, and PP to achieve optimal throughput.

What is the best way to start pretraining a Llama 3.1 8B model on 8 GPUs?▼

The best way to start pretraining a Llama 3.1 8B model on 8 GPUs is to run the provided training script with the specific Llama 3.1 8B configuration file, which sets up the distributed environment natively in PyTorch.