distributed-llm-pretraining-torchtitan

Configure and launch distributed LLM pretraining with PyTorch-native 4D parallelism.

1|Updated May 12, 2026

Search Tags:#llm #distributed #pytorch #parallelism #pretraining #torchtitan #fsdp2

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the complexity of training large-scale language models from scratch by providing a PyTorch-native framework that handles distributed parallelism, memory optimization, and hardware-specific acceleration.

Core Features & Use Cases

Composable 4D Parallelism: Orchestrates FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism to train models ranging from 8B to 405B+ parameters.
Hardware Acceleration: Leverages Float8 training and torch.compile to achieve significant speedups on H100 GPU clusters.
Use Case: Use this skill to configure and launch a distributed training job for a Llama 3.1 70B model across a multi-node GPU cluster using SLURM.

Quick Start

Use the distributed-llm-pretraining-torchtitan skill to launch a training job on 8 GPUs using the llama3_8b.toml configuration file.

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 from scratch using PyTorch distributed parallelism?▼

To pretrain a large language model from scratch, this framework orchestrates PyTorch-native 4D parallelism, combining FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism to scale training across multi-node GPU clusters.

What is 4D parallelism and when do I need it for LLM pretraining?▼

4D parallelism is the simultaneous application of FSDP2, Tensor, Pipeline, and Context Parallelism. You need it for LLM pretraining when scaling models from 8B to 405B+ parameters across 8 to 512+ GPUs where memory and compute bottlenecks occur.

Can I use Float8 and torch.compile to accelerate distributed pretraining on H100 clusters?▼

Yes, you can accelerate distributed pretraining on H100 clusters by leveraging native Float8 training and torch.compile optimizations, which provide significant speedups and memory efficiency during large-scale model training.

What's the best way to configure a distributed training job for a Llama 70B model on a SLURM cluster?▼

The best way to configure distributed training for a Llama 70B model on a SLURM cluster is using the framework's composable 4D parallelism techniques, which manage distributed parallelism and hardware acceleration across multi-node environments.

Do I need specific PyTorch versions to run FSDP2 and 4D parallelism training?▼

Yes, you need PyTorch version 2.6.0 or higher to run FSDP2 and 4D parallelism training, along with torchtitan 0.2.0 and torchao 0.5.0 or above for high-performance computing environments and interoperable checkpoints.

How does this framework handle checkpoint interoperability during scalable training workflows?▼

This framework provides interoperable checkpoints for scalable training workflows by utilizing PyTorch-native distributed parallelism techniques, ensuring training states are consistently saved and restored across large-scale multi-node GPU configurations.