ColossalAI

Colossal-AI

Colossal-AI: Making large AI models cheaper, faster, and more accessible

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Instantly Run Colossal-AI on Enterprise-Grade GPUs

Skip the setup. Access a powerful, pre-configured Colossal-AI environment on HPC-AI Cloud.

Train your models and scale your AI workload in one click!

• NVIDIA Blackwell B200s: Experience the next generation of AI performance (See Benchmarks). Now available on cloud from $2.47/hr.
• Cost-Effective H200 Cluster: Get premier performance with on-demand rental from just $1.99/hr.

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Instant Access Top Open Models at Half the Cost

Skip the hassle. Access powerful, long-context LLMs seamlessly through HPC-AI Model APIs.

Build your AI agents, chatbots, and RAG applications with HPC-AI Model APIs!

• Latest & Greatest Models: Experience state-of-the-art performance with Kimi 2.5, MiniMax 2.5, and GLM 5.1. Perfect for massive 2M+ context windows and complex coding tasks.

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Colossal-AI Benchmark

To see how these performance gains translate to real-world applications, we conducted a large language model training benchmark using Colossal-AI on Llama-like models. The tests were run on both 8-card and 16-card configurations for 7B and 70B models, respectively.

GPU	GPUs	Model Size	Parallelism	Batch Size per DP	Seqlen	Throughput	TFLOPS/GPU	Peak Mem(MiB)
H200	8	7B	zero2(dp8)	36	4096	17.13 samp/s	534.18	119040.02
H200	16	70B	zero2	48	4096	3.27 samp/s	469.1	150032.23
B200	8	7B	zero1(dp2)+tp2+pp4	128	4096	25.83 samp/s	805.69	100119.77
H200	16	70B	zero1(dp2)+tp2+pp4	128	4096	5.66 samp/s	811.79	100072.02

The results from the Colossal-AI benchmark provide the most practical insight. For the 7B model on 8 cards, the B200 achieved a 50% higher throughput and a significant increase in TFLOPS per GPU. For the 70B model on 16 cards, the B200 again demonstrated a clear advantage, with over 70% higher throughput and TFLOPS per GPU. These numbers show that the B200's performance gains translate directly to faster training times for large-scale models.

Latest News

• [2025/02] DeepSeek 671B Fine-Tuning Guide Revealed—Unlock the Upgraded DeepSeek Suite with One Click, AI Players Ecstatic!
• [2024/12] The development cost of video generation models has saved by 50%! Open-source solutions are now available with H200 GPU vouchers [code] [vouchers]
• [2024/10] How to build a low-cost Sora-like app? Solutions for you
• [2024/09] Singapore Startup HPC-AI Tech Secures 50 Million USD in Series A Funding to Build the Video Generation AI Model and GPU Platform
• [2024/09] Reducing AI Large Model Training Costs by 30% Requires Just a Single Line of Code From FP8 Mixed Precision Training Upgrades
• [2024/06] Open-Sora Continues Open Source: Generate Any 16-Second 720p HD Video with One Click, Model Weights Ready to Use
• [2024/05] Large AI Models Inference Speed Doubled, Colossal-Inference Open Source Release
• [2024/04] Open-Sora Unveils Major Upgrade: Embracing Open Source with Single-Shot 16-Second Video Generation and 720p Resolution
• [2024/04] Most cost-effective solutions for inference, fine-tuning and pretraining, tailored to LLaMA3 series

Table of Contents

Why Colossal-AI

Prof. James Demmel (UC Berkeley): Colossal-AI makes training AI models efficient, easy, and scalable.

Features

Colossal-AI provides a collection of parallel components for you. We aim to support you to write your distributed deep learning models just like how you write your model on your laptop. We provide user-friendly tools to kickstart distributed training and inference in a few lines.

• Parallelism strategies

  • Data Parallelism
  • Pipeline Parallelism
  • 1D, 2D, 2.5D, 3D Tensor Parallelism
  • Sequence Parallelism
  • Zero Redundancy Optimizer (ZeRO)
  • Auto-Parallelism

• Heterogeneous Memory Management

  • PatrickStar

• Friendly Usage

  • Parallelism based on the configuration file

Colossal-AI in the Real World

Open-Sora

Open-Sora：Revealing Complete Model Parameters, Training Details, and Everything for Sora-like Video Generation Models [code] [blog] [Model weights] [Demo] [GPU Cloud Playground] [OpenSora Image]

Colossal-LLaMA-2

[GPU Cloud Playground] [LLaMA3 Image]

• 7B: One half-day of training using a few hundred dollars yields similar results to mainstream large models, open-source and commercial-free domain-specific LLM solution. [code] [blog] [HuggingFace model weights] [Modelscope model weights]

• 13B: Construct refined 13B private model with just $5000 USD. [code] [blog] [HuggingFace model weights] [Modelscope model weights]

Model	Backbone	Tokens Consumed	MMLU (5-shot)	CMMLU (5-shot)	AGIEval (5-shot)	GAOKAO (0-shot)	CEval (5-shot)
Baichuan-7B	-	1.2T	42.32 (42.30)	44.53 (44.02)	38.72	36.74	42.80
Baichuan-13B-Base	-	1.4T	50.51 (51.60)	55.73 (55.30)	47.20	51.41	53.60
Baichuan2-7B-Base	-	2.6T	46.97 (54.16)	57.67 (57.07)	45.76	52.60	54.00
Baichuan2-13B-Base	-	2.6T	54.84 (59.17)	62.62 (61.97)	52.08	58.25	58.10
ChatGLM-6B	-	1.0T	39.67 (40.63)	41.17 (-)	40.10	36.53	38.90
ChatGLM2-6B	-	1.4T	44.74 (45.46)	49.40 (-)	46.36	45.49	51.70
InternLM-7B	-	1.6T	46.70 (51.00)	52.00 (-)	44.77	61.64	52.80
Qwen-7B	-	2.2T	54.29 (56.70)	56.03 (58.80)	52.47	56.42	59.60
Llama-2-7B	-	2.0T	44.47 (45.30)	32.97 (-)	32.60	25.46	-
Linly-AI/Chinese-LLaMA-2-7B-hf	Llama-2-7B	1.0T	37.43	29.92	32.00	27.57	-
wenge-research/yayi-7b-llama2	Llama-2-7B	-	38.56	31.52	30.99	25.95	-
ziqingyang/chinese-llama-2-7b	Llama-2-7B	-	33.86	34.69	34.52	25.18	34.2
TigerResearch/tigerbot-7b-base	Llama-2-7B	0.3T	43.73	42.04	37.64	30.61	-
LinkSoul/Chinese-Llama-2-7b	Llama-2-7B	-	48.41	38.31	38.45	27.72	-
FlagAlpha/Atom-7B	Llama-2-7B	0.1T	49.96	41.10	39.83	33.00	-
IDEA-CCNL/Ziya-LLaMA-13B-v1.1	Llama-13B	0.11T	50.25	40.99	40.04	30.54	-
Colossal-LLaMA-2-7b-base	Llama-2-7B	0.0085T	53.06	49.89	51.48	58.82	50.2
Colossal-LLaMA-2-13b-base	Llama-2-13B	0.025T	56.42	61.80	54.69	69.53	60.3

ColossalChat

ColossalChat: An open-source solution for cloning ChatGPT with a complete RLHF pipeline. [code] [blog] [demo] [tutorial]

• Up to 10 times faster for RLHF PPO Stage3 Training

• Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference

• Up to 10.3x growth in model capacity on one GPU
• A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU)

• Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU
• Keep at a sufficiently high running speed

AIGC

Acceleration of AIGC (AI-Generated Content) models such as Stable Diffusion v1 and Stable Diffusion v2.

• Training: Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060).

• DreamBooth Fine-tuning: Personalize your model using just 3-5 images of the desired subject.

• Inference: Reduce inference GPU memory consumption by 2.5x.

Biomedicine

Acceleration of AlphaFold Protein Structure

• FastFold: Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.

• FastFold with Intel: 3x inference acceleration and 39% cost reduce.

• xTrimoMultimer: accelerating structure prediction of protein monomers and multimer by 11x.

Parallel Training Demo

LLaMA3

• 70 billion parameter LLaMA3 model training accelerated by 18% [code] [GPU Cloud Playground] [LLaMA3 Image]

LLaMA2

• 70 billion parameter LLaMA2 model training accelerated by 195% [code] [blog]

LLaMA1

• 65-billion-parameter large model pretraining accelerated by 38% [code] [blog]

MoE

• Enhanced MoE parallelism, Open-source MoE model training can be 9 times more efficient [code] [blog]

GPT-3

• Save 50% GPU resources and 10.7% acceleration

GPT-2

• 11x lower GPU memory consumption, and superlinear scaling efficiency with Tensor Parallelism

• 24x larger model size on the same hardware
• over 3x acceleration

BERT

• 2x faster training, or 50% longer sequence length

PaLM

• PaLM-colossalai: Scalable implementation of Google's Pathways Language Model (PaLM).

OPT

• Open Pretrained Transformer (OPT), a 175-Billion parameter AI language model released by Meta, which stimulates AI programmers to perform various downstream tasks and application deployments because of public pre-trained model weights.
• 45% speedup fine-tuning OPT at low cost in lines. [Example] [Online Serving]

Please visit our documentation and examples for more details.

ViT

• 14x larger batch size, and 5x faster training for Tensor Parallelism = 64

Recommendation System Models

• Cached Embedding, utilize software cache to train larger embedding tables with a smaller GPU memory budget.

Single GPU Training Demo

GPT-2

• 20x larger model size on the same hardware

• 120x larger model size on the same hardware (RTX 3080)

PaLM

• 34x larger model size on the same hardware

Inference

Colossal-Inference

• Large AI models inference speed doubled, compared to the offline inference performance of vLLM in some cases. [code] [blog] [GPU Cloud Playground] [LLaMA3 Image]

Grok-1

• 314 Billion Parameter Grok-1 Inference Accelerated by 3.8x, an easy-to-use Python + PyTorch + HuggingFace version for Inference.

[code] [blog] [HuggingFace Grok-1 PyTorch model weights] [ModelScope Grok-1 PyTorch model weights]

SwiftInfer

• SwiftInfer: Inference performance improved by 46%, open source solution breaks the length limit of LLM for multi-round conversations

Installation

Requirements:

• 2.2 <= PyTorch < 2.14
• Python >= 3.10 and < 3.13
• CUDA >= 11.0
• NVIDIA GPU Compute Capability >= 7.0 (V100/RTX20 and higher)
• Linux OS

If you encounter any problem with installation, you may want to raise an issue in this repository.

Install from PyPI

You can easily install Colossal-AI with the following command. By default, we do not build PyTorch extensions during installation.

pip install colossalai

Note: only Linux is supported for now.

However, if you want to build the PyTorch extensions during installation, you can set BUILD_EXT=1.

BUILD_EXT=1 pip install --no-binary=colossalai --no-build-isolation colossalai

Otherwise, CUDA kernels will be built during runtime when you actually need them.

We also keep releasing the nightly version to PyPI every week. This allows you to access the unreleased features and bug fixes in the main branch. Installation can be made via

pip install colossalai-nightly

Download From Source

The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problems. :)

git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI

# install colossalai
pip install .

By default, we do not compile CUDA/C++ kernels. ColossalAI will build them during runtime. If you want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):

BUILD_EXT=1 pip install --no-build-isolation .

For Users with CUDA 10.2, you can still build ColossalAI from source. However, you need to manually download the cub library and copy it to the corresponding directory.

# clone the repository
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI

# download the cub library
wget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip
unzip 1.8.0.zip
cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/

# install
BUILD_EXT=1 pip install --no-build-isolation .

Use Docker

Pull from DockerHub

You can directly pull the docker image from our DockerHub page. The image is automatically uploaded upon release.

Build On Your Own

Run the following command to build a docker image from Dockerfile provided.

Building Colossal-AI from scratch requires GPU support, you need to use Nvidia Docker Runtime as the default when doing docker build. More details can be found here. We recommend you install Colossal-AI from our project page directly.

cd ColossalAI
docker build -t colossalai ./docker

Run the following command to start the docker container in interactive mode.

docker run -ti --gpus all --rm --ipc=host colossalai bash

Community

Join the Colossal-AI community on Forum, Slack, and WeChat(微信) to share your suggestions, feedback, and questions with our engineering team.

Contributing

Referring to the successful attempts of BLOOM and Stable Diffusion, any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models!

You may contact us or participate in the following ways:

1. Leaving a Star ⭐ to show your like and support. Thanks!
2. Posting an issue, or submitting a PR on GitHub follow the guideline in Contributing
3. Send your official proposal to email [email protected]

Thanks so much to all of our amazing contributors!

CI/CD

We leverage the power of GitHub Actions to automate our development, release and deployment workflows. Please check out this documentation on how the automated workflows are operated.

Cite Us

This project is inspired by some related projects (some by our team and some by other organizations). We would like to credit these amazing projects as listed in the Reference List.

To cite this project, you can use the following BibTeX citation.

@inproceedings{10.1145/3605573.3605613,
author = {Li, Shenggui and Liu, Hongxin and Bian, Zhengda and Fang, Jiarui and Huang, Haichen and Liu, Yuliang and Wang, Boxiang and You, Yang},
title = {Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
year = {2023},
isbn = {9798400708435},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3605573.3605613},
doi = {10.1145/3605573.3605613},
abstract = {The success of Transformer models has pushed the deep learning model scale to billions of parameters, but the memory limitation of a single GPU has led to an urgent need for training on multi-GPU clusters. However, the best practice for choosing the optimal parallel strategy is still lacking, as it requires domain expertise in both deep learning and parallel computing. The Colossal-AI system addressed the above challenge by introducing a unified interface to scale your sequential code of model training to distributed environments. It supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer. Compared to the baseline system, Colossal-AI can achieve up to 2.76 times training speedup on large-scale models.},
booktitle = {Proceedings of the 52nd International Conference on Parallel Processing},
pages = {766–775},
numpages = {10},
keywords = {datasets, gaze detection, text tagging, neural networks},
location = {Salt Lake City, UT, USA},
series = {ICPP '23}
}

Colossal-AI has been accepted as official tutorial by top conferences NeurIPS, SC, AAAI, PPoPP, CVPR, ISC, NVIDIA GTC ,etc.