--- title: "ggml vs hub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ggml-org-ggml-vs-tensorflow-hub" tools: ["ggml-org-ggml", "tensorflow-hub"] --- # ggml vs hub *GraphCanon updated Aug 17, 2026* ## 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. [ggml](https://github.com/ggml-org/ggml) reports 15k GitHub stars, 1.8k forks, and 346 open issues, last pushed Aug 14, 2026. [hub](https://tensorflow.org/hub) has 3.5k stars, 1.6k forks, and 11 open issues, last pushed Jan 17, 2025. Figures are from public GitHub metadata via [ggml's repository](https://github.com/ggml-org/ggml) and [hub's repository](https://github.com/tensorflow/hub). | | [ggml](/tools/ggml-org-ggml.md) | [hub](/tools/tensorflow-hub.md) | | --- | --- | --- | | Tagline | Tensor library for machine learning | A library for transfer learning by reusing parts of TensorFlow models. | | Stars | 15,185 | 3,522 | | Forks | 1,780 | 1,644 | | Open issues | 346 | 11 | | Language | C++ | Python | | Adopt for | 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 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 | - | - | | Runtime | - | - | | License | 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 is licensed under Apache-2.0, allowing for broad use in both open source and commercial projects. | | Categories | Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ggml](/tools/ggml-org-ggml.md) | [hub](/tools/tensorflow-hub.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 2d | 551d | | Open issues (now) | 346 | 11 | | Stars delta | +183 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/ggml-org-ggml/trust.md) | [trust report](/tools/tensorflow-hub/trust.md) | ## Decision facts: ggml - **Pricing:** freemium - 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. - **Adopt for:** 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. - **License detail:** ggml is distributed under the MIT License, which permits free use and modification for both private and commercial uses with attribution to the authors. ## Decision facts: hub - **Pricing:** freemium - 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. - **Adopt for:** 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. - **License detail:** hub is licensed under Apache-2.0, allowing for broad use in both open source and commercial projects. ## Choose when ### 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 ### 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 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 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. ## 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](/tools/ggml-org-ggml/alternatives) and [hub alternatives](/tools/tensorflow-hub/alternatives) ([ggml markdown twin](/tools/ggml-org-ggml/alternatives.md), [hub markdown twin](/tools/tensorflow-hub/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/ggml-org-ggml-vs-tensorflow-hub.md) 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](/tools/ggml-org-ggml/trust); [hub trust report](/tools/tensorflow-hub/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ggml-org-ggml`](/api/graphcanon/graph?tool=ggml-org-ggml) - 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/_