--- title: "gpt-neox vs ColossalAI" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eleutherai-gpt-neox-vs-hpcaitech-colossalai" tools: ["eleutherai-gpt-neox", "hpcaitech-colossalai"] --- # gpt-neox vs ColossalAI *GraphCanon updated Jul 11, 2026* ## Verdict Pick gpt-neox when tags unique to gpt-neox: gpt-3, python, deepspeed-library, transformers; pick ColossalAI when tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training. [gpt-neox](https://www.eleuther.ai/) reports 7.4k GitHub stars, 1.1k forks, and 104 open issues, last pushed Jun 11, 2026. [ColossalAI](https://www.colossalai.org) has 41k stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. Figures are from public GitHub metadata via [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox) and [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI). | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Tagline | An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries | Making large AI models cheaper, faster and more accessible | | Stars | 7,443 | 41,408 | | Forks | 1,123 | 4,504 | | Open issues | 104 | 501 | | Language | Python | Python | | Adopt for | - | ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Model Training | Model Training, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 29d | 46d | | Open issues (now) | 104 | 501 | | Full report | [trust report](/tools/eleutherai-gpt-neox/trust.md) | [trust report](/tools/hpcaitech-colossalai/trust.md) | ## Decision facts: ColossalAI - **Adopt for:** ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. ## Choose when ### Choose gpt-neox if… - Tags unique to gpt-neox: gpt-3, python, deepspeed-library, transformers. - More recently updated (last pushed Jun 11, 2026). ### Choose ColossalAI if… - Tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training. - Also covers Inference & Serving. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ## When NOT to use gpt-neox - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use ColossalAI - You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. - Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). - You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ## Common questions ### What is the difference between gpt-neox and ColossalAI? gpt-neox: An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries. ColossalAI: Making large AI models cheaper, faster and more accessible. See the comparison table for live GitHub stats and shared categories. ### When should I choose gpt-neox over ColossalAI? Choose gpt-neox over ColossalAI when Tags unique to gpt-neox: gpt-3, python, deepspeed-library, transformers; More recently updated (last pushed Jun 11, 2026). ### When should I choose ColossalAI over gpt-neox? Choose ColossalAI over gpt-neox when Tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I avoid gpt-neox? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid ColossalAI? You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ### Is gpt-neox or ColossalAI more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 7,443). Stars measure visibility, not whether either tool fits your constraints. ### Are gpt-neox and ColossalAI open source? Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, ColossalAI: Apache-2.0). ### Where can I find alternatives to gpt-neox or ColossalAI? GraphCanon lists graph-backed alternatives at [gpt-neox alternatives](/tools/eleutherai-gpt-neox/alternatives) and [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) ([gpt-neox markdown twin](/tools/eleutherai-gpt-neox/alternatives.md), [ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/eleutherai-gpt-neox-vs-hpcaitech-colossalai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gpt-neox or ColossalAI? gpt-neox: Active. ColossalAI: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for gpt-neox and ColossalAI? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gpt-neox trust report](/tools/eleutherai-gpt-neox/trust); [ColossalAI trust report](/tools/hpcaitech-colossalai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eleutherai-gpt-neox`](/api/graphcanon/graph?tool=eleutherai-gpt-neox) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_