--- title: "ColossalAI vs fastDeploy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-notai-tech-fastdeploy" tools: ["hpcaitech-colossalai", "notai-tech-fastdeploy"] --- # ColossalAI vs fastDeploy *GraphCanon updated Jul 15, 2026* ## Verdict Pick ColossalAI when license: ColossalAI is Apache-2.0, fastDeploy is MIT; pick fastDeploy when license: fastDeploy is MIT, ColossalAI is Apache-2.0. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 499 open issues, last pushed Jul 13, 2026. [fastDeploy](https://github.com/notAI-tech/fastDeploy) has 105 stars, 17 forks, and 0 open issues, last pushed Feb 10, 2026. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [fastDeploy's repository](https://github.com/notAI-tech/fastDeploy). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | Deploy DL/ ML inference pipelines with minimal extra code. | | Stars | 41,413 | 105 | | Forks | 4,502 | 17 | | Open issues | 499 | 0 | | 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 | MIT | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 154d | | Open issues (now) | 499 | 0 | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/notai-tech-fastdeploy/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 ColossalAI if… - License: ColossalAI is Apache-2.0, fastDeploy is MIT. - Tags unique to ColossalAI: ai, big model, data-parallelism, distributed-computing. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### Choose fastDeploy if… - License: fastDeploy is MIT, ColossalAI is Apache-2.0. - Tags unique to fastDeploy: docker, falcon, gevent, gunicorn. - Also covers Speech & Audio. ## 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. ## When NOT to use fastDeploy - Last GitHub push was 155 days ago (slowing maintenance, Feb 10, 2026). Validate activity before betting a new project on fastDeploy. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between ColossalAI and fastDeploy? ColossalAI: Making large AI models cheaper, faster and more accessible. fastDeploy: Deploy DL/ ML inference pipelines with minimal extra code.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over fastDeploy? Choose ColossalAI over fastDeploy when License: ColossalAI is Apache-2.0, fastDeploy is MIT; Tags unique to ColossalAI: ai, big model, data-parallelism, distributed-computing; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I choose fastDeploy over ColossalAI? Choose fastDeploy over ColossalAI when License: fastDeploy is MIT, ColossalAI is Apache-2.0; Tags unique to fastDeploy: docker, falcon, gevent, gunicorn; Also covers Speech & Audio. ### 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. ### When should I avoid fastDeploy? Last GitHub push was 155 days ago (slowing maintenance, Feb 10, 2026). Validate activity before betting a new project on fastDeploy. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is ColossalAI or fastDeploy more popular on GitHub? ColossalAI has more GitHub stars (41,413 vs 105). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and fastDeploy open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, fastDeploy: MIT). ### Where can I find alternatives to ColossalAI or fastDeploy? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [fastDeploy alternatives](/tools/notai-tech-fastdeploy/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [fastDeploy markdown twin](/tools/notai-tech-fastdeploy/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/hpcaitech-colossalai-vs-notai-tech-fastdeploy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or fastDeploy? ColossalAI: Very active. fastDeploy: Slowing. 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 ColossalAI and fastDeploy? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [fastDeploy trust report](/tools/notai-tech-fastdeploy/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hpcaitech-colossalai`](/api/graphcanon/graph?tool=hpcaitech-colossalai) - 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/_