# hpcaitech/ColossalAI

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41,349 stars · 4,533 forks · Python · apache-2.0

## Links

- GitHub: https://github.com/hpcaitech/ColossalAI
- Homepage: https://www.colossalai.org
- awesome-repositories: https://awesome-repositories.com/repository/hpcaitech-colossalai.md

## Topics

`ai` `big-model` `data-parallelism` `deep-learning` `distributed-computing` `foundation-models` `heterogeneous-training` `hpc` `inference` `large-scale` `model-parallelism` `pipeline-parallelism`

## Description

ColossalAI is a distributed deep learning framework designed for training and deploying massive artificial intelligence models across clusters of hardware accelerators. It functions as a parallel computing engine that partitions model workloads and data across multiple processors to maximize memory efficiency and throughput.

The platform distinguishes itself through a comprehensive suite of parallelization strategies, including multi-dimensional tensor parallelism and pipeline-based model parallelism, which segment neural network layers and stages across devices. To support large-scale generative models in production, it provides a distributed inference runtime that utilizes dynamic request batching and optimized communication primitives to manage high volumes of concurrent traffic and minimize latency.

The framework incorporates a large model optimization suite that enables the execution of complex models on limited hardware. This includes heterogeneous memory offloading, which moves parameters between GPU memory and system storage, and kernel-level computation optimizations that replace standard operations to reduce memory overhead. These capabilities facilitate both the training of massive models and the deployment of generative applications in production environments.

## Tags

### Artificial Intelligence & ML

- [Distributed Deep Learning Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-deep-learning-frameworks.md) — Provides a unified platform for training and deploying massive artificial intelligence models across clusters of hardware accelerators.
- [Distributed Training Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-orchestrators.md) — Trains large-scale models across multiple graphics processors by splitting the workload to reduce memory usage. ([source](https://cdn.jsdelivr.net/gh/hpcaitech/ColossalAI@main/README.md))
- [Large-Scale Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-model-training.md) — Trains massive artificial intelligence models that exceed the memory capacity of a single hardware device.
- [Distributed Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-inference-runtimes.md) — Provides a production-ready environment for serving large-scale generative models by distributing request processing.
- [Parallel Computing Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/parallel-computing-engines.md) — Partitions large model workloads and data across multiple processors to maximize memory efficiency and throughput.
- [Tensor Parallelism Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/tensor-parallelism-frameworks.md) — Splits individual model layers across multiple hardware accelerators to reduce the memory footprint of massive neural network parameters.
- [Distributed Inference Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-inference-frameworks.md) — Serves large-scale generative models in production by splitting workloads across multiple hardware accelerators.
- [Distributed Inference Services](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-inference-services.md) — Distributes large model workloads across multiple processors using parallel computing strategies to handle high volumes of traffic. ([source](https://colossalai.org/docs/advanced_tutorials/opt_service))
- [Distributed Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks.md) — Coordinates data synchronization between multiple processing units using optimized communication primitives to minimize latency.
- [Model Optimization Suites](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization-suites.md) — Provides a collection of memory management and kernel acceleration techniques to fit massive neural networks onto limited hardware.
- [Pipeline Parallelism Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/pipeline-parallelism-tools.md) — Segments deep learning models into sequential stages distributed across different devices to balance computational load.
- [Inference Acceleration Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-acceleration-engines.md) — Runs large language models faster by using optimized processing kernels and memory management techniques. ([source](https://cdn.jsdelivr.net/gh/hpcaitech/ColossalAI@main/README.md))
- [Large Model Training Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/large-model-training-utilities.md) — Fits massive models onto limited hardware by using memory-efficient techniques and disk-based storage offloading. ([source](https://cdn.jsdelivr.net/gh/hpcaitech/ColossalAI@main/README.md))
- [Memory-Efficient Deep Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/memory-efficient-deep-learning.md) — Optimizes computational resources and memory usage to enable the execution of complex models on limited hardware.
- [Memory Management Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/memory-management-strategies.md) — Moves model parameters and optimizer states between GPU memory and system RAM or disk to accommodate large models.
- [Inference Latency Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-latency-optimizers.md) — Improves response times for generation tasks by configuring request grouping and memory caching. ([source](https://colossalai.org/docs/advanced_tutorials/opt_service))
- [Kernel Optimization Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/kernel-optimization-libraries.md) — Replaces standard operations with custom high-performance kernels to accelerate mathematical calculations.
- [Inference Optimization Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-optimization-tools.md) — Groups incoming inference requests into optimized execution blocks to maximize hardware utilization.
- [Parallel AI Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/parallel-ai-workflows.md) — Implements advanced data and tensor parallelism strategies to accelerate development and deployment cycles.

### DevOps & Infrastructure

- [Model Deployment Platforms](https://awesome-repositories.com/f/devops-infrastructure/model-deployment-platforms.md) — Launches pre-trained or custom generative models into production environments for specialized tasks. ([source](https://cdn.jsdelivr.net/gh/hpcaitech/ColossalAI@main/README.md))