# hpcaitech/ColossalAI

Making large AI models cheaper, faster and more accessible

Repository: https://github.com/hpcaitech/ColossalAI
Canonical: https://ross.abutalabs.com/products/colossalai
Homepage: https://www.colossalai.org
Language: Python
License: Apache-2.0
License Family: permissive
Topics: deep-learning, hpc, large-scale, data-parallelism, pipeline-parallelism, model-parallelism, ai, big-model, distributed-computing, inference, heterogeneous-training, foundation-models
Last push: 2026-08-24T17:46:02+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 28, longevity 100
- inputs: {"age_days": 1770, "days_push": 9, "days_rel": 455, "gap_med": 31, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 41441, forks 4496 (observed 2026-08-28T04:12:08.554154+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: llm-training, deep-learning, machine-learning, llm-inference, gpu-computing
- domain: deep-learning, large-language-models, machine-learning, microservices, gpu-computing
- platform: python, cloud
- tags: distributed-training, tensor-parallelism, pipeline-parallelism, data-parallelism, mixture-of-experts, heterogeneous-memory, zero-optimizer, pytorch, model-parallelism, offloading, linux, gpu, docker

## Member repositories
- hpcaitech/ColossalAI (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:08.554154+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:23:04.714008+00:00, confidence not recorded.
  - readme: https://github.com/hpcaitech/ColossalAI (fetched 2026-08-28T04:12:08.554154+00:00, sha 9bd0d5dcb32c)
  - homepage: https://www.colossalai.org (fetched 2026-08-29T07:46:27.351013+00:00, sha 11d9578299af)
  - site_page: https://colossalai.org/docs/get_started/installation (fetched 2026-08-29T07:46:27.355025+00:00, sha 64af4a94e4b0)
  - site_page: https://colossalai.org/docs/get_started/run_demo (fetched 2026-08-29T07:46:27.357354+00:00, sha 757618311811)
  - site_page: https://colossalai.org/docs/concepts/colossalai_overview (fetched 2026-08-29T07:46:27.359260+00:00, sha 534e84caa67d)
  - site_page: https://colossalai.org/docs/concepts/distributed_training (fetched 2026-08-29T07:46:27.361466+00:00, sha 083e8f4401c9)
  - site_page: https://colossalai.org/docs/concepts/paradigms_of_parallelism (fetched 2026-08-29T07:46:27.364179+00:00, sha 8631c252a848)
  - site_page: https://colossalai.org/docs/advanced_tutorials/train_gpt_using_hybrid_parallelism (fetched 2026-08-29T07:46:27.387161+00:00, sha f49549f014b8)
  - site_page: https://colossalai.org/docs/advanced_tutorials/meet_gemini (fetched 2026-08-29T07:46:27.402664+00:00, sha 56d5c9ad568d)
  - site_page: https://colossalai.org/docs/basics/command_line_tool (fetched 2026-08-29T07:46:27.404909+00:00, sha f95595647fd3)
- Data as of 2026-08-30T08:39:29.467469+00:00.