hpcaitech/ColossalAI

Health v2 · maintenance only

Adoption not part of the score

41430 stars · 4485 forks observed · 2026-10-16

What it is AI-extracted, prompt v1, taxonomy v1, 2026-09-28, confidence 0.97

ColossalAI is a Python library that provides efficient parallelization techniques for training large deep learning models. It supports data, tensor, pipeline, and mixture-of-experts parallelism across multiple GPUs.

Use cases

• train a GPT-like model on a multi‑GPU cluster
• scale ResNet training with data parallelism on several nodes
• apply tensor and pipeline parallelism to a large transformer model
• run mixture‑of‑experts models with model parallelism
• use heterogeneous memory management to train models larger than GPU memory

When to choose

• You need to train very large neural networks that exceed a single GPU's memory.
• You want to leverage multiple GPUs or nodes with advanced parallelism strategies.
• You require a unified API for mixed‑precision, gradient accumulation, and offloading.

When to avoid

• Your workload fits on a single GPU and does not need distributed training.
• You are using non‑Linux environments or lack CUDA‑compatible hardware.
• You prefer a higher‑level framework that abstracts away parallelism details.

Facets

library · maturity active

machine-learning deep-learning large-language-models python large-model model-parallelism data-parallelism pipeline-parallelism gpu distributed-training heterogeneous-memory distributed-computing linux

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