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Infrastructure & MLOps

Litgpt

LitGPT is an open-source framework developed by Lightning AI designed for pretraining, finetuning, and deploying over 20 large language models (LLMs) using from-scratch implementations without abstractions. It supports scalable training across 1 to over 1000 GPUs or TPUs and offers configurable workflows through YAML files, enabling users to customize parameters such as batch sizes and LoRA finetuning. The tool emphasizes resource optimization with features like Flash Attention, Fully Sharded Data Parallel (FSDP), and mixed precision support (fp4/8/16/32). The codebase is modular and readable, consisting of over 44,000 lines of Python code, and is accompanied by extensive documentation and tutorials aimed at developers and researchers working with natural language processing models. LitGPT operates primarily via command-line interfaces and requires manual setup through cloning its GitHub repository and installing dependencies. It is distributed under an open-source license and does not have a dedicated standalone website, with primary resources hosted on GitHub and Lightning AI's documentation portal.

Updated Jan 7, 2026open-source
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Overview

LitGPT is an open-source tool for scalable pretraining, finetuning, and deployment of large language models with multi-GPU/TPU support.

Pricing
open-source
Category
Infrastructure & MLOps
Company
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Key Features
01
Supports training and deployment across 1 to over 1000 GPUs or TPUs with optimizations for distributed computing.
02
Enables users to customize training parameters such as micro batch sizes and LoRA finetuning through YAML configuration files.
03
Incorporates Flash Attention, Fully Sharded Data Parallel (FSDP), and mixed precision (fp4/8/16/32) to reduce GPU memory usage during training.
04
Provides recipes and command-line tools for pretraining, finetuning, and deploying a variety of LLMs including EleutherAI/pythia-160m.
05
Allows users to train models on their own datasets with straightforward command-line commands and readable code for modifications.
06
Offers extensive guides and beginner-friendly workflows to assist users in setting up and using the tool effectively.
Real-World Use Cases

Pretraining Large Language Models

Researchers or developers can pretrain LLMs from scratch on custom datasets using scalable multi-GPU setups.

Finetuning with LoRA

Users can finetune existing models with parameter-efficient LoRA techniques to adapt models to specific tasks or domains.

Deploying Custom LLMs

After training or finetuning, models can be deployed and tested locally via command-line interfaces for inference.

Quick Start
1
Clone the Repository
Clone the LitGPT GitHub repository to your local machine using git.
2
Install Dependencies
Install required Python packages via pip as specified in the repository documentation.
3
Download a Tokenizer
Use the command litgpt download EleutherAI/pythia-160m --tokenizer_only True to download the tokenizer for a model.
4
Pretrain a Model
Run a pretraining session with a command like litgpt pretrain EleutherAI/pythia-160m --tokenizer_dir EleutherAI/pythia-160m --data TextFiles --data.train_data_path "custom_texts/" --train.max_tokens 10_000_000 --out_dir out/custom-model.
5
Finetune with LoRA
Execute finetuning using LoRA with a command such as litgpt finetune lora --config config_hub/finetune/phi-2/lora.yaml.
6
Test the Model
Test the trained or finetuned model locally using litgpt chat out/custom-model/final.
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Strategic Context for Litgpt

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Pricing
Model: open-source

LitGPT is free to use under an open-source license with no paid plans.

Assessment
Strengths
  • Supports scaling across 1 to over 1000 GPUs/TPUs with multi-GPU optimization.
  • Includes memory-saving features such as Flash Attention, FSDP, LoRA, and QLoRA.
  • Provides YAML configuration recipes for over 20 large language models and custom datasets.
  • Codebase is readable and beginner-friendly with no abstractions.
  • Comprehensive documentation and tutorials are available.
Limitations
  • Requires cloning the GitHub repository and manual installation of dependencies via pip.
  • Operates primarily through command-line interfaces without a built-in graphical user interface.
Alternatives
localAI→Llama File (llamafile)→LM Studio→PrivateGPT→