Comparison
ggml vs ort
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 ort if ort accelerates ML inference and training tasks for ONNX models with high-performance Rust operations.
Markdown twin · ggml alternatives · ort alternatives
GraphCanon updated today
Trust & integrity
| Signal | ggml | ort |
|---|---|---|
| Maintenance | Very active (2d since push) As of today · github_public_v1 | Very active (0d 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
- ort
- Fast ML inference and training for ONNX models in Rust
Stars
- ggml
- 15k
- ort
- 2.4k
Forks
- ggml
- 1.8k
- ort
- 256
Open issues
- ggml
- 346
- ort
- 1
Language
- ggml
- C++
- ort
- Rust
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.
- ort
- ort accelerates ML inference and training tasks for ONNX models with high-performance Rust operations
Persona
- ggml
- -
- ort
- -
Runtime
- ggml
- -
- ort
- -
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.
- ort
- Apache-2.0
Last pushed
- ggml
- Aug 14, 2026
- ort
- Jul 23, 2026
Categories
- ggml
- Model Training
- ort
- Inference & Serving, Model Training
Trust and health
Days since push
- ggml
- 2d
- ort
- 0d
Open issues (now)
- ggml
- 346
- ort
- 1
Stars delta
- ggml
- +183 (30d)
- ort
- Unknown
Open issues delta
- ggml
- 0 (30d)
- ort
- Unknown
OSV dependency advisories
- ggml
- Published findings
- ort
- No lockfile (source not queried)
Full report
- ggml
- Trust report
- ort
- Trust report
Choose ggml if…
- ggml is primarily C++; ort is Rust.
- License: ggml is MIT, ort 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 ort if…
- ort is primarily Rust; ggml is C++.
- License: ort is Apache-2.0, ggml is MIT.
- Tags unique to ort: ai, fine-tuning, inference, onnx.
- Also covers Inference & Serving.
- When your project involves ONNX models that require fast inference times or efficient fine-tuning
When NOT to use ort
- When the primary development language is not compatible with Rust bindings
- For projects requiring broad model support beyond ONNX, as ort specializes only in ONNX models and does not cover a wide array of formats like some competitors might
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 (pykeio/ort) · observed Jul 24, 2026
- GitHub forks (pykeio/ort) · observed Jul 24, 2026
- Last push (pykeio/ort) · observed Jul 23, 2026
- License file (Apache-2.0) · observed Jul 24, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ggml 15k · ort 2.4k (synced Aug 17, 2026).
Common questions
- What is the difference between ggml and ort?
- ggml: Tensor library for machine learning. ort: Fast ML inference and training for ONNX models in Rust. See the comparison table for live GitHub stats and shared categories.
- When should I choose ggml over ort?
- Choose ggml over ort when ggml is primarily C++; ort is Rust; License: ggml is MIT, ort 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 ort over ggml?
- Choose ort over ggml when ort is primarily Rust; ggml is C++; License: ort is Apache-2.0, ggml is MIT; Tags unique to ort: ai, fine-tuning, inference, onnx; Also covers Inference & Serving; When your project involves ONNX models that require fast inference times or efficient fine-tuning.
- 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 ort?
- When the primary development language is not compatible with Rust bindings For projects requiring broad model support beyond ONNX, as ort specializes only in ONNX models and does not cover a wide array of formats like some competitors might
- Is ggml or ort more popular on GitHub?
- ggml has more GitHub stars (15,185 vs 2,416). Stars measure visibility, not whether either tool fits your constraints.
- Are ggml and ort open source?
- Yes - both are open-source projects on GitHub (ggml: MIT, ort: Apache-2.0).
- Where can I find alternatives to ggml or ort?
- GraphCanon lists graph-backed alternatives at ggml alternatives and ort alternatives (ggml markdown twin, ort 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 ort?
- ggml: Very active. ort: 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 ggml and ort?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ggml trust report; ort trust report.