Colossal-AI

Colossal-AI is an open-source distributed AI framework with 41,000+ stars on GitHub that aims to make training large AI models cheaper, faster and more accessible.

Overview

Built by HPC-AI Tech, Colossal-AI gives PyTorch users an integrated toolkit for large-scale training: data, tensor, pipeline and sequence parallelism, sharded optimizer states, and heterogeneous training that spreads work across GPUs, CPUs and NVMe storage. Optimized kernels and memory techniques squeeze more out of each accelerator, so clusters spend their time computing instead of waiting on memory. Beyond raw training, the project includes ColossalChat, a toolkit for RLHF-style fine-tuning of chat models, and a long list of reference implementations for popular architectures such as Llama-class models. It targets foundation-model workloads end to end — pretraining, fine-tuning and inference — and remains under active development, with updates continuing through October 2026. For teams without hardware, the maintainers also operate HPC-AI Cloud, a GPU rental and model-API service promoted from the project site.

Key Features

• Multi-dimensional parallelism – data, tensor, pipeline and sequence parallelism combined in one framework.
• Sharded training states – distribute optimizer and parameter memory across devices to fit larger models.
• Heterogeneous training – use GPU, CPU and NVMe together for models that exceed GPU memory.
• ColossalChat – RLHF and instruction-tuning toolkit for building chat models.
• Optimized kernels – high-performance attention and operator implementations for common architectures.
• Model zoo and examples – ready-made training recipes for Llama-class and other popular models.
• PyTorch-friendly API – designed to slot into existing PyTorch training code with modest changes.

💳 Pricing

The Colossal-AI framework is free and open source under the Apache License 2.0 for self-hosting on your own hardware. The team behind it also runs HPC-AI Cloud, a paid GPU cloud and model-API service, if you would rather rent capacity than buy GPUs.

Who Is It For

Research labs, AI startups and platform teams pretraining or fine-tuning large models on GPU clusters, who want an open, PyTorch-native alternative to proprietary training stacks.