Comparison
handy-ollama vs ColossalAI
Verdict
Pick handy-ollama if handy-ollama is a guide for deploying large language models using Ollama on CPU systems via Jupyter Notebooks; 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.
Markdown twin · handy-ollama alternatives · ColossalAI alternatives
GraphCanon updated Sep 20, 2026
11views this month
vs
Trust & integrity
| Signal | handy-ollama | ColossalAI |
|---|---|---|
| Maintenance | Slowing (247d since push) As of Sep 20, 2026 · github_public_v1 | Very active (6d since push) As of Sep 6, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 6, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- handy-ollama
- Hands-On Ollama with CPU for Large Model Deployment
- ColossalAI
- Making large AI models cheaper, faster and more accessible
Stars
- handy-ollama
- 2.5k
- ColossalAI
- 41k
Forks
- handy-ollama
- 321
- ColossalAI
- 4.5k
Open issues
- handy-ollama
- 8
- ColossalAI
- 505
Language
- handy-ollama
- Jupyter Notebook
- ColossalAI
- Python
Adopt for
- handy-ollama
- handy-ollama is a guide for deploying large language models using Ollama on CPU systems via Jupyter Notebooks.
- ColossalAI
- ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models.
Persona
- handy-ollama
- -
- ColossalAI
- -
Runtime
- handy-ollama
- -
- ColossalAI
- -
License
- handy-ollama
- handy-ollama is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
- ColossalAI
- Apache-2.0
Last pushed
- handy-ollama
- Jan 15, 2026
- ColossalAI
- Aug 31, 2026
Categories
- handy-ollama
- Inference & Serving, Model Training
- ColossalAI
- Inference & Serving, Model Training
Trust and health
Maintenance
- handy-ollama
- Slowing (36%)
- ColossalAI
- Very active (96%)
Days since push
- handy-ollama
- 247d
- ColossalAI
- 6d
Open issues (now)
- handy-ollama
- 8
- ColossalAI
- 505
Stars delta
- handy-ollama
- +33 (30d)
- ColossalAI
- +11 (30d)
Full report
- handy-ollama
- Trust report
- ColossalAI
- Trust report
Choose handy-ollama if…
- handy-ollama is primarily Jupyter Notebook; ColossalAI is Python.
- License: handy-ollama is Other, ColossalAI is Apache-2.0.
- Requirements: Requires Ollama library for operations..
- Tags unique to handy-ollama: agent, gguf, langchain, large-language-models.
- Use handy-ollama when you require specific guidance on deploying large models with the Ollama library exclusively on CPUs, as opposed to GPU-based alternatives.
When NOT to use handy-ollama
- Avoid handy-ollama if you need support for deploying models on GPU or other hardware that is not specifically CPUs.
- Do not use this guide if comprehensive tutorials in languages other than English are necessary, as the content appears to be primarily in Chinese and English.
Choose ColossalAI if…
- ColossalAI is primarily Python; handy-ollama is Jupyter Notebook.
- License: ColossalAI is Apache-2.0, handy-ollama is Other.
- Tags unique to ColossalAI: ai, big model, data-parallelism, deep-learning.
- You require handling extremely large AI models with massive context windows, such as over 2M tokens.
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (datawhalechina/handy-ollama) · observed Sep 20, 2026
- GitHub forks (datawhalechina/handy-ollama) · observed Sep 20, 2026
- Last push (datawhalechina/handy-ollama) · observed Jan 15, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (hpcaitech/ColossalAI) · observed Sep 20, 2026
- GitHub forks (hpcaitech/ColossalAI) · observed Sep 20, 2026
- Last push (hpcaitech/ColossalAI) · observed Aug 31, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: handy-ollama 2.5k · ColossalAI 41k (synced Sep 20, 2026).
Common questions
- What is the difference between handy-ollama and ColossalAI?
- handy-ollama: Hands-On Ollama with CPU for Large Model Deployment. 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 handy-ollama over ColossalAI?
- Choose handy-ollama over ColossalAI when handy-ollama is primarily Jupyter Notebook; ColossalAI is Python; License: handy-ollama is Other, ColossalAI is Apache-2.0; Requirements: Requires Ollama library for operations.; Tags unique to handy-ollama: agent, gguf, langchain, large-language-models; Use handy-ollama when you require specific guidance on deploying large models with the Ollama library exclusively on CPUs, as opposed to GPU-based alternatives.
- When should I choose ColossalAI over handy-ollama?
- Choose ColossalAI over handy-ollama when ColossalAI is primarily Python; handy-ollama is Jupyter Notebook; License: ColossalAI is Apache-2.0, handy-ollama is Other; Tags unique to ColossalAI: ai, big model, data-parallelism, deep-learning; You require handling extremely large AI models with massive context windows, such as over 2M tokens.
- When should I avoid handy-ollama?
- Avoid handy-ollama if you need support for deploying models on GPU or other hardware that is not specifically CPUs. Do not use this guide if comprehensive tutorials in languages other than English are necessary, as the content appears to be primarily in Chinese and English.
- 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 handy-ollama or ColossalAI more popular on GitHub?
- ColossalAI has more GitHub stars (41,443 vs 2,532). Stars measure visibility, not whether either tool fits your constraints.
- Are handy-ollama and ColossalAI open source?
- Yes - both are open-source projects on GitHub (handy-ollama: Other, ColossalAI: Apache-2.0).
- Where can I find alternatives to handy-ollama or ColossalAI?
- GraphCanon lists graph-backed alternatives at handy-ollama alternatives and ColossalAI alternatives (handy-ollama markdown twin, ColossalAI markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, handy-ollama or ColossalAI?
- handy-ollama: Slowing. ColossalAI: 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 handy-ollama and ColossalAI?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: handy-ollama trust report; ColossalAI trust report.