hpcaitech/ColossalAI

Health v2 · maintenance only

Adoption not part of the score

41441 stars · 4496 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Colossal-AI is an integrated large-scale deep learning training system built on PyTorch that provides efficient parallelization techniques including data, tensor, pipeline, sequence, and MoE parallelism. It also offers heterogeneous memory management (Gemini) to train large models that exceed GPU memory by leveraging CPU RAM and NVMe storage.

Use cases

• train large language models on multiple GPUs
• fine-tune GPT-2 with hybrid parallelism
• train models larger than GPU memory using CPU offloading
• scale deep learning training across a GPU cluster
• apply tensor or pipeline parallelism to my PyTorch model
• train mixture-of-experts models efficiently
• reduce the cost of large model training
• run distributed training on a single GPU machine

When to choose

• you need to train or fine-tune models too large for a single GPU
• you want a unified PyTorch-native alternative to Megatron-LM or DeepSpeed
• you need to combine multiple parallelism strategies (data, tensor, pipeline, ZeRO)
• you have limited GPU memory and want heterogeneous CPU/GPU memory management
• you want to experiment with MoE or sequence parallelism

When to avoid

• you only train small models that fit on one GPU with no need for parallelism
• you need Windows or macOS support - only Linux is supported
• you want a fully managed training service rather than a library you integrate into your code
• your workflow depends on non-PyTorch frameworks like JAX or TensorFlow

Facets

library · maturity active

llm-training deep-learning machine-learning llm-inference gpu-computing deep-learning large-language-models machine-learning microservices gpu-computing python cloud distributed-training tensor-parallelism pipeline-parallelism data-parallelism mixture-of-experts heterogeneous-memory zero-optimizer pytorch model-parallelism offloading linux gpu docker

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markdown · JSON · MCP: product_card(name="hpcaitech/ColossalAI")