Comparison
ggml vs hub
Verdict
Pick ggml if ggml is a C++ based tensor library that supports automatic-differentiation and large-language-models, making it suitable for performance-critical applications where language flexibility and low-level control are key; pick hub if hub is specifically tailored to Python developers who wish to incorporate transfer learning into their TensorFlow projects with pre-trained model components for applications such as image classification.
Markdown twin · ggml alternatives · hub alternatives
GraphCanon updated today
Trust & integrity
| Signal | ggml | hub |
|---|---|---|
| Maintenance | Very active (2d since push) As of today · github_public_v1 | Dormant (551d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- ggml
- Tensor library for machine learning
- hub
- A library for transfer learning by reusing parts of TensorFlow models.
Stars
- ggml
- 15k
- hub
- 3.5k
Forks
- ggml
- 1.8k
- hub
- 1.6k
Open issues
- ggml
- 346
- hub
- 11
Language
- ggml
- C++
- hub
- Python
Adopt for
- ggml
- ggml is a C++ based tensor library that supports automatic-differentiation and large-language-models, making it suitable for performance-critical applications where language flexibility and low-level control are key.
- hub
- hub is specifically tailored to Python developers who wish to incorporate transfer learning into their TensorFlow projects with pre-trained model components for applications such as image classification.
Persona
- ggml
- -
- hub
- -
Runtime
- ggml
- -
- hub
- -
License
- ggml
- ggml is distributed under the MIT License, which permits free use and modification for both private and commercial uses with attribution to the authors.
- hub
- hub is licensed under Apache-2.0, allowing for broad use in both open source and commercial projects.
Last pushed
- ggml
- Aug 14, 2026
- hub
- Jan 17, 2025
Categories
- ggml
- Model Training
- hub
- Data & Retrieval, Model Training
Trust and health
Maintenance
- ggml
- Very active (96%)
- hub
- Dormant (18%)
Days since push
- ggml
- 2d
- hub
- 551d
Open issues (now)
- ggml
- 346
- hub
- 11
Stars delta
- ggml
- +183 (30d)
- hub
- Unknown
Open issues delta
- ggml
- 0 (30d)
- hub
- Unknown
OSV dependency advisories
- ggml
- Published findings
- hub
- No lockfile (source not queried)
Full report
- ggml
- Trust report
- hub
- Trust report
Choose ggml if…
- ggml is primarily C++; hub is Python.
- License: ggml is MIT, hub is Apache-2.0.
- Pricing: Free to use with optional support or consulting services that can be sought from contributors or third parties..
- Requirements: Requires setting up a Python virtual environment and installing dependencies, as per provided README instructions; however, this is for interfacing with the C++; core does not affect its use in C++ projects..
- Tags unique to ggml: automatic-differentiation, large language models, tensor-algebra.
- - When you need to work with large language models or require automatic differentiation capabilities in your machine learning projects specifically within the C++ ecosystem
When NOT to use ggml
- - Avoid if your project requires a more extensive set of tools and ease-of-use found in higher-level frameworks (e.g., TensorFlow or PyTorch)
- - If you prefer environments where the majority of community support and libraries are available in Python rather than C++
Choose hub if…
- hub is primarily Python; ggml is C++.
- License: hub is Apache-2.0, ggml is MIT.
- Pricing: The core functionalities of hub are free to use with an open-source license; however, additional services or enterprise support might incur costs..
- Requirements: Requires a Python environment and TensorFlow installation to operate..
- Tags unique to hub: embeddings, image-classification, ml, python.
- Also covers Data & Retrieval.
- When you need to leverage existing TensorFlow models and integrate specific parts of them for tasks like embedding or image-classification without retraining the entire model from scratch.
When NOT to use hub
- When working strictly with non-TensorFlow frameworks such as PyTorch or MXNet, as hub is built specifically for enhancing and reusing models within TensorFlow.
- If your project requires a more generalized approach to machine-learning without reliance on pre-existing model components, focusing instead on training models from the ground up.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ggml-org/ggml) · observed Aug 17, 2026
- GitHub forks (ggml-org/ggml) · observed Aug 17, 2026
- Last push (ggml-org/ggml) · observed Aug 14, 2026
- License file (MIT) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorflow/hub) · observed Jul 22, 2026
- GitHub forks (tensorflow/hub) · observed Jul 22, 2026
- Last push (tensorflow/hub) · observed Jan 17, 2025
- License file (Apache-2.0) · observed Jul 22, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ggml 15k · hub 3.5k (synced Aug 17, 2026).
Common questions
- What is the difference between ggml and hub?
- ggml: Tensor library for machine learning. hub: A library for transfer learning by reusing parts of TensorFlow models.. See the comparison table for live GitHub stats and shared categories.
- When should I choose ggml over hub?
- Choose ggml over hub when ggml is primarily C++; hub is Python; License: ggml is MIT, hub is Apache-2.0; Pricing: Free to use with optional support or consulting services that can be sought from contributors or third parties.; Requirements: Requires setting up a Python virtual environment and installing dependencies, as per provided README instructions; however, this is for interfacing with the C++; core does not affect its use in C++ projects.; Tags unique to ggml: automatic-differentiation, large language models, tensor-algebra; - When you need to work with large language models or require automatic differentiation capabilities in your machine learning projects specifically within the C++ ecosystem.
- When should I choose hub over ggml?
- Choose hub over ggml when hub is primarily Python; ggml is C++; License: hub is Apache-2.0, ggml is MIT; Pricing: The core functionalities of hub are free to use with an open-source license; however, additional services or enterprise support might incur costs.; Requirements: Requires a Python environment and TensorFlow installation to operate.; Tags unique to hub: embeddings, image-classification, ml, python; Also covers Data & Retrieval; When you need to leverage existing TensorFlow models and integrate specific parts of them for tasks like embedding or image-classification without retraining the entire model from scratch.
- When should I avoid ggml?
- - Avoid if your project requires a more extensive set of tools and ease-of-use found in higher-level frameworks (e.g., TensorFlow or PyTorch) - If you prefer environments where the majority of community support and libraries are available in Python rather than C++
- When should I avoid hub?
- When working strictly with non-TensorFlow frameworks such as PyTorch or MXNet, as hub is built specifically for enhancing and reusing models within TensorFlow. If your project requires a more generalized approach to machine-learning without reliance on pre-existing model components, focusing instead on training models from the ground up.
- Is ggml or hub more popular on GitHub?
- ggml has more GitHub stars (15,185 vs 3,522). Stars measure visibility, not whether either tool fits your constraints.
- Are ggml and hub open source?
- Yes - both are open-source projects on GitHub (ggml: MIT, hub: Apache-2.0).
- Where can I find alternatives to ggml or hub?
- GraphCanon lists graph-backed alternatives at ggml alternatives and hub alternatives (ggml markdown twin, hub 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, ggml or hub?
- ggml: Very active. hub: Dormant. 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 ggml and hub?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ggml trust report; hub trust report.