--- title: "model_search vs ColossalAI" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/google-model-search-vs-hpcaitech-colossalai" tools: ["google-model-search", "hpcaitech-colossalai"] --- # model_search vs ColossalAI *GraphCanon updated Jul 11, 2026* ## Verdict Pick model_search when tags unique to model_search: python; pick ColossalAI when tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training. [model_search](https://github.com/google/model_search) reports 3.2k GitHub stars, 549 forks, and 53 open issues, last pushed Jul 30, 2024. [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 [model_search's repository](https://github.com/google/model_search) and [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI). | | [model_search](/tools/google-model-search.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Tagline | model_search | Making large AI models cheaper, faster and more accessible | | Stars | 3,241 | 41,408 | | Forks | 549 | 4,504 | | Open issues | 53 | 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, Evaluation & Observability | Model Training, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [model_search](/tools/google-model-search.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Steady (60%) | | Days since push | 711d | 46d | | Archived on GitHub | Yes | No | | Open issues (now) | 53 | 501 | | Security scan | 268 low (268 low) | No lockfile | | Full report | [trust report](/tools/google-model-search/trust.md) | [trust report](/tools/hpcaitech-colossalai/trust.md) | ## Shared compatibility - **Python**: [model_search](/tools/google-model-search.md) - Python runtime; [ColossalAI](/tools/hpcaitech-colossalai.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. ## Choose when ### Choose model_search if… - Tags unique to model_search: python. - Also covers Evaluation & Observability. - Leaner open-issue backlog (53). ### 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 model_search - model_search is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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 model_search and ColossalAI? model_search: model_search. 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 model_search over ColossalAI? Choose model_search over ColossalAI when Tags unique to model_search: python; Also covers Evaluation & Observability; Leaner open-issue backlog (53). ### When should I choose ColossalAI over model_search? Choose ColossalAI over model_search 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 model_search? model_search is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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 model_search or ColossalAI more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 3,241). Stars measure visibility, not whether either tool fits your constraints. ### Are model_search and ColossalAI open source? Yes - both are open-source projects on GitHub (model_search: Apache-2.0, ColossalAI: Apache-2.0). ### Where can I find alternatives to model_search or ColossalAI? GraphCanon lists graph-backed alternatives at [model_search alternatives](/tools/google-model-search/alternatives) and [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) ([model_search markdown twin](/tools/google-model-search/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/google-model-search-vs-hpcaitech-colossalai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, model_search or ColossalAI? model_search: Archived. 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 model_search and ColossalAI? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [model_search trust report](/tools/google-model-search/trust); [ColossalAI trust report](/tools/hpcaitech-colossalai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=google-model-search`](/api/graphcanon/graph?tool=google-model-search) - 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/_