hpcaitech/ColossalAI

Making large AI models cheaper, faster and more accessible

What it solves

Colossal-AI tackles the high cost and complexity of training and running very large AI models. Training a 70-billion-parameter model normally requires expensive, specialized hardware and deep expertise in distributed systems. Colossal-AI makes large-model AI cheaper, faster, and more accessible by providing a collection of parallel computing tools that let developers scale their models across many GPUs with just a few lines of code.

How it works

Colossal-AI offers multiple parallelism strategies that split the work of training or inference across GPUs. These include data parallelism (each GPU sees different data), pipeline parallelism (each GPU handles a different stage of the model), and various forms of tensor parallelism (splitting the model's layers across GPUs). It also implements the ZeRO optimizer to reduce memory usage, and an auto-parallelism system that finds the best parallel configuration automatically. For inference, Colossal-Inference doubles speed, and SwiftInfer handles long multi-round conversations. The project also includes real-world applications like Open-Sora for video generation, Colossal-LLaMA-2 for cheap domain-specific LLM training, and ColossalChat for building ChatGPT clones with a full RLHF pipeline.

Who it's for

Colossal-AI is aimed at machine learning engineers, researchers, and companies that need to train or deploy large language models, diffusion models, or other massive AI systems. It suits teams that want to reduce hardware costs, scale across multiple GPUs, or fine-tune models like LLaMA on a budget.

Highlights

  • Supports multiple parallelism strategies: data, pipeline, 1D/2D/2.5D/3D tensor, sequence, and ZeRO
  • Auto-parallelism automatically finds optimal configurations
  • Heterogeneous memory management (PatrickStar) to fit large models on limited GPU memory
  • Demonstrated speedups: RLHF training up to 10x faster, single-server training 7.73x faster
  • Real-world examples: Open-Sora (video generation), Colossal-LLaMA-2 (cheap domain LLMs), ColossalChat (ChatGPT clone), Stable Diffusion acceleration
  • Costs as little as a few hundred dollars to train a 7B model to competitive performance

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