distributed-llm-pretraining-torchtitan

Name: distributed-llm-pretraining-torchtitan
Availability: InStock
Author: samuelmukoti

Distribute LLM pretraining across multi-GPU clusters with 4D parallelism.

Updated Apr 21, 2026

Search Tags:#distributed-training #tensor-parallel #torchtitan #float8 #fsdp2 #llm-pretraining

Ready to run

npx skills add https://github.com/samuelmukoti/myai-agent --skill distributed-llm-pretraining-torchtitan-samuelmukoti

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: distributed-llm-pretraining-torchtitan
Source: https://github.com/samuelmukoti/myai-agent/tree/main/optional-skills/mlops/torchtitan
Command: npx skills add https://github.com/samuelmukoti/myai-agent --skill distributed-llm-pretraining-torchtitan-samuelmukoti

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrates scalable pretraining of large language models by providing a PyTorch-native implementation with 4D parallelism (FSDP2, TP, PP, CP) across multi-GPU clusters, simplifying setup, scaling, and checkpoint interoperability.

Core Features & Use Cases

4D parallelism across FSDP2, tensor, pipeline, and context parallelism to scale pretraining from 8 to 512+ GPUs.
Float8 support with torch.compile for speedups on modern GPUs.
Distributed checkpointing and interoperability with torchtune/HuggingFace for seamless resume and deployment.
Use Case: pretraining Llama 3.1, DeepSeek V3, or custom models at large scale.

Quick Start

Start distributed LLM pretraining across 8 to 512+ GPUs using TorchTitan.

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 scale LLM pretraining across a multi-GPU cluster using PyTorch?▼

You scale LLM pretraining across a multi-GPU cluster using PyTorch by applying 4D parallelism (FSDP2, tensor, pipeline, and context parallelism) to distribute workloads efficiently from 8 to 512+ GPUs.

Can I use Float8 and torch.compile to speed up large language model pretraining?▼

Yes, you can use Float8 support with torch.compile to achieve significant speedups on modern GPUs during large language model pretraining. This combination optimizes memory usage and accelerates compute-heavy operations.

Does this distributed training approach support interoperability with HuggingFace checkpoints?▼

Yes, distributed training with this approach supports interoperability with HuggingFace checkpoints. It utilizes distributed checkpointing and integrates with torchtune to ensure seamless resume and deployment capabilities.

What is the best way to pretrain Llama 3.1 or DeepSeek V3 at scale?▼

The best way to pretrain Llama 3.1 or DeepSeek V3 at scale is using a PyTorch-native implementation with 4D parallelism and distributed checkpointing, which simplifies setup and scaling across multi-GPU clusters.

Do I need 4D parallelism to pretrain custom models on 512+ GPUs?▼

Yes, you need 4D parallelism (FSDP2, TP, PP, CP) to pretrain custom models on 512+ GPUs. It orchestrates scalable pretraining by distributing the workload across multi-GPU clusters efficiently.

How does FSDP2 work with tensor and pipeline parallelism during large-scale pretraining?▼

FSDP2 works with tensor and pipeline parallelism during large-scale pretraining by sharding model parameters, activations, and gradients across devices, while context parallelism manages sequence lengths to optimize multi-GPU cluster memory.