--- title: "ColossalAI vs private-gpt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-zylon-ai-private-gpt" tools: ["hpcaitech-colossalai", "zylon-ai-private-gpt"] --- # ColossalAI vs private-gpt *GraphCanon updated Jul 11, 2026* ## Verdict Pick ColossalAI if colossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models; pick private-gpt if privateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities,. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. [private-gpt](https://www.zylon.ai/private-gpt) has 57k stars, 7.6k forks, and 5 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [private-gpt's repository](https://github.com/zylon-ai/private-gpt). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [private-gpt](/tools/zylon-ai-private-gpt.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | Complete API layer for private AI applications on local models | | Stars | 41,408 | 57,329 | | Forks | 4,504 | 7,598 | | Open issues | 501 | 5 | | 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. | PrivateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities, | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [private-gpt](/tools/zylon-ai-private-gpt.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 46d | 0d | | Open issues (now) | 501 | 5 | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/zylon-ai-private-gpt/trust.md) | ## Shared compatibility - **Python**: [ColossalAI](/tools/hpcaitech-colossalai.md) - Python runtime; [private-gpt](/tools/zylon-ai-private-gpt.md) - Python runtime ## 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. ## Decision facts: private-gpt - **Requirements:** Min 8 GB RAM; Requires Docker - **Adopt for:** PrivateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities, ## Choose when ### Choose ColossalAI if… - Tags unique to ColossalAI: big-model, data-parallelism, deep-learning, distributed-computing. - Also covers Model Training. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### Choose private-gpt if… - Requirements: Min 8 GB RAM; Requires Docker. - Tags unique to private-gpt: ai-tools, local-models, mcp, on-premise. - private-gpt ships Docker support for self-hosted deployment. - - You need to deploy and operationalize your own locally-run models without relying on cloud APIs. ## 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 private-gpt - - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services. - - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations. - - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively. ## Common questions ### What is the difference between ColossalAI and private-gpt? ColossalAI: Making large AI models cheaper, faster and more accessible. private-gpt: Complete API layer for private AI applications on local models. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over private-gpt? Choose ColossalAI over private-gpt when Tags unique to ColossalAI: big-model, data-parallelism, deep-learning, distributed-computing; Also covers Model Training; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I choose private-gpt over ColossalAI? Choose private-gpt over ColossalAI when Requirements: Min 8 GB RAM; Requires Docker; Tags unique to private-gpt: ai-tools, local-models, mcp, on-premise; private-gpt ships Docker support for self-hosted deployment; - You need to deploy and operationalize your own locally-run models without relying on cloud APIs. ### 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 private-gpt? - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services. - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations. - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively. ### Is ColossalAI or private-gpt more popular on GitHub? private-gpt has more GitHub stars (57,329 vs 41,408). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and private-gpt open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, private-gpt: Apache-2.0). ### Where can I find alternatives to ColossalAI or private-gpt? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [private-gpt alternatives](/tools/zylon-ai-private-gpt/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [private-gpt markdown twin](/tools/zylon-ai-private-gpt/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-zylon-ai-private-gpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or private-gpt? ColossalAI: Steady. private-gpt: Very active. 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 private-gpt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [private-gpt trust report](/tools/zylon-ai-private-gpt/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/_