Comparison
handy-ollama vs DeepSpeed
Verdict
Pick handy-ollama if handy-ollama is a guide for deploying large language models using Ollama on CPU systems via Jupyter Notebooks; pick DeepSpeed if decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression.
Markdown twin · handy-ollama alternatives · DeepSpeed alternatives
GraphCanon updated Sep 6, 2026
11views this month
vs
Trust & integrity
| Signal | handy-ollama | DeepSpeed |
|---|---|---|
| Maintenance | Slowing (210d since push) As of Aug 14, 2026 · github_public_v1 | Very active (0d since push) As of Sep 6, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Aug 14, 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
- DeepSpeed
- Deep learning optimization library for efficient distributed training and inference
Stars
- handy-ollama
- 2.5k
- DeepSpeed
- 43k
Forks
- handy-ollama
- 315
- DeepSpeed
- 5.0k
Open issues
- handy-ollama
- 8
- DeepSpeed
- 1.4k
Language
- handy-ollama
- Jupyter Notebook
- DeepSpeed
- Python
Adopt for
- handy-ollama
- handy-ollama is a guide for deploying large language models using Ollama on CPU systems via Jupyter Notebooks.
- DeepSpeed
- Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression.
Persona
- handy-ollama
- -
- DeepSpeed
- -
Runtime
- handy-ollama
- -
- DeepSpeed
- -
License
- handy-ollama
- handy-ollama is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
- DeepSpeed
- Apache-2.0
Last pushed
- handy-ollama
- Jan 15, 2026
- DeepSpeed
- Sep 6, 2026
Categories
- handy-ollama
- Inference & Serving, Model Training
- DeepSpeed
- Inference & Serving, Model Training
Trust and health
Maintenance
- handy-ollama
- Slowing (36%)
- DeepSpeed
- Very active (96%)
Days since push
- handy-ollama
- 210d
- DeepSpeed
- 0d
Open issues (now)
- handy-ollama
- 8
- DeepSpeed
- 1.4k
Stars delta
- handy-ollama
- Unknown
- DeepSpeed
- +195 (30d)
Open issues delta
- handy-ollama
- Unknown
- DeepSpeed
- +77 (30d)
Full report
- handy-ollama
- Trust report
- DeepSpeed
- Trust report
Choose handy-ollama if…
- handy-ollama is primarily Jupyter Notebook; DeepSpeed is Python.
- License: handy-ollama is Other, DeepSpeed 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 DeepSpeed if…
- DeepSpeed is primarily Python; handy-ollama is Jupyter Notebook.
- License: DeepSpeed is Apache-2.0, handy-ollama is Other.
- Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning.
- - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters)
When NOT to use DeepSpeed
- - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs
- - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
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 Aug 14, 2026
- GitHub forks (datawhalechina/handy-ollama) · observed Aug 14, 2026
- Last push (datawhalechina/handy-ollama) · observed Jan 15, 2026
- License file (Other) · observed Aug 14, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (deepspeedai/DeepSpeed) · observed Sep 6, 2026
- GitHub forks (deepspeedai/DeepSpeed) · observed Sep 6, 2026
- Last push (deepspeedai/DeepSpeed) · observed Sep 6, 2026
- License file (Apache-2.0) · observed Sep 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: handy-ollama 2.5k · DeepSpeed 43k (synced Aug 14, 2026).
Common questions
- What is the difference between handy-ollama and DeepSpeed?
- handy-ollama: Hands-On Ollama with CPU for Large Model Deployment. DeepSpeed: Deep learning optimization library for efficient distributed training and inference. See the comparison table for live GitHub stats and shared categories.
- When should I choose handy-ollama over DeepSpeed?
- Choose handy-ollama over DeepSpeed when handy-ollama is primarily Jupyter Notebook; DeepSpeed is Python; License: handy-ollama is Other, DeepSpeed 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 DeepSpeed over handy-ollama?
- Choose DeepSpeed over handy-ollama when DeepSpeed is primarily Python; handy-ollama is Jupyter Notebook; License: DeepSpeed is Apache-2.0, handy-ollama is Other; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters).
- 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 DeepSpeed?
- - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
- Is handy-ollama or DeepSpeed more popular on GitHub?
- DeepSpeed has more GitHub stars (43,065 vs 2,499). Stars measure visibility, not whether either tool fits your constraints.
- Are handy-ollama and DeepSpeed open source?
- Yes - both are open-source projects on GitHub (handy-ollama: Other, DeepSpeed: Apache-2.0).
- Where can I find alternatives to handy-ollama or DeepSpeed?
- GraphCanon lists graph-backed alternatives at handy-ollama alternatives and DeepSpeed alternatives (handy-ollama markdown twin, DeepSpeed 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 DeepSpeed?
- handy-ollama: Slowing. DeepSpeed: 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 DeepSpeed?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: handy-ollama trust report; DeepSpeed trust report.