hpcaitech/colossalai

Colossal-AI is a deep learning framework designed to make training and running large AI models dramatically cheaper and faster than conventional methods. The core problem it solves is that state-of-the-art AI models like GPT or LLaMA have billions of parameters and require enormous amounts of GPU memory and compute, making them accessible only to well-funded organizations. Colossal-AI addresses this by spreading the work across many GPUs simultaneously using a variety of parallelism strategies, essentially treating multiple machines as one giant computer. The system achieves this through several complementary techniques. Data parallelism divides your training dataset across GPUs so each one processes a different batch. Tensor parallelism splits the model's internal weight matrices across devices. Pipeline parallelism assigns different layers of the model to different GPUs, passing activations through like an assembly line. A memory optimization technique called ZeRO (Zero Redundancy Optimizer) intelligently partitions optimizer states, gradients, and parameters to minimize redundant memory usage. Together these approaches allow training models that simply would not fit on any single GPU. You would reach for Colossal-AI when you want to train or fine-tune large foundation models such as LLaMA, DeepSeek, or video generation models on a cluster of GPUs, but need to control costs and maximize throughput. It is particularly useful for researchers and engineers at companies that cannot afford the largest proprietary cloud clusters. The library also supports heterogeneous training, mixing CPUs and GPUs to squeeze even more efficiency out of available hardware. The entire project is written in Python and integrates tightly with PyTorch, the dominant deep learning framework. It supports modern hardware including NVIDIA H200 and B200 GPUs and provides a Python API that can slot into existing PyTorch training loops with minimal code changes.