Comparison
gpt-neox vs ggml
Verdict
Pick gpt-neox if gPT-NeoX from EleutherAI leverages GPU-based model parallelism via Megatron and DeepSpeed libraries to facilitate the training of large-scale autoregressive transformers in Python, under an Apache-2.0 license; 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.
Markdown twin · gpt-neox alternatives · ggml alternatives
GraphCanon updated 4d
Trust & integrity
| Signal | gpt-neox | ggml |
|---|---|---|
| Maintenance | Steady (56d since push) As of 2w · github_public_v1 | Very active (2d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- gpt-neox
- Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries
- ggml
- Tensor library for machine learning
Stars
- gpt-neox
- 7.5k
- ggml
- 15k
Forks
- gpt-neox
- 1.1k
- ggml
- 1.8k
Open issues
- gpt-neox
- 111
- ggml
- 346
Language
- gpt-neox
- Python
- ggml
- C++
Adopt for
- gpt-neox
- GPT-NeoX from EleutherAI leverages GPU-based model parallelism via Megatron and DeepSpeed libraries to facilitate the training of large-scale autoregressive transformers in Python, under an Apache-2.0 license.
- 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.
Persona
- gpt-neox
- -
- ggml
- -
Runtime
- gpt-neox
- -
- ggml
- -
License
- gpt-neox
- The tool is licensed under Apache-2.0, allowing permissive use but emphasizing that derivative works must preserve copyright headers and licenses as per their origins
- 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.
Last pushed
- gpt-neox
- Jun 11, 2026
- ggml
- Aug 14, 2026
Categories
- gpt-neox
- LLM Frameworks, Model Training
- ggml
- Model Training
Trust and health
Maintenance
- gpt-neox
- Steady (60%)
- ggml
- Very active (96%)
Days since push
- gpt-neox
- 56d
- ggml
- 2d
Open issues (now)
- gpt-neox
- 111
- ggml
- 346
Stars delta
- gpt-neox
- Unknown
- ggml
- +183 (30d)
Open issues delta
- gpt-neox
- Unknown
- ggml
- 0 (30d)
OSV dependency advisories
- gpt-neox
- No lockfile (source not queried)
- ggml
- Published findings
Full report
- gpt-neox
- Trust report
- ggml
- Trust report
Choose gpt-neox if…
- gpt-neox is primarily Python; ggml is C++.
- License: gpt-neox is Apache-2.0, ggml is MIT.
- Pricing: Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations..
- Tags unique to gpt-neox: deepspeed-library, gpt-3, language-model, transformers.
- Also covers LLM Frameworks.
- - When your project requires a framework based on state-of-the-art libraries like Megatron and DeepSpeed that are optimized for large GPU clusters.
When NOT to use gpt-neox
- - In scenarios where minimal hardware resources, such as a single low-memory GPU or CPU-only environments, are available for training due to GPT-NeoX's requirement for a large-scale infrastructure.
- - If your project is limited by the Apache License terms or requires proprietary codebases without open-source contributions and modifications from external parties.
Choose ggml if…
- ggml is primarily C++; gpt-neox is Python.
- License: ggml is MIT, gpt-neox 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, machine-learning, 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++
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (EleutherAI/gpt-neox) · observed Aug 7, 2026
- GitHub forks (EleutherAI/gpt-neox) · observed Aug 7, 2026
- Last push (EleutherAI/gpt-neox) · observed Jun 11, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: gpt-neox 7.5k · ggml 15k (synced Aug 7, 2026).
Common questions
- What is the difference between gpt-neox and ggml?
- gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. ggml: Tensor library for machine learning. See the comparison table for live GitHub stats and shared categories.
- When should I choose gpt-neox over ggml?
- Choose gpt-neox over ggml when gpt-neox is primarily Python; ggml is C++; License: gpt-neox is Apache-2.0, ggml is MIT; Pricing: Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations.; Tags unique to gpt-neox: deepspeed-library, gpt-3, language-model, transformers; Also covers LLM Frameworks; - When your project requires a framework based on state-of-the-art libraries like Megatron and DeepSpeed that are optimized for large GPU clusters.
- When should I choose ggml over gpt-neox?
- Choose ggml over gpt-neox when ggml is primarily C++; gpt-neox is Python; License: ggml is MIT, gpt-neox 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, machine-learning, 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 avoid gpt-neox?
- - In scenarios where minimal hardware resources, such as a single low-memory GPU or CPU-only environments, are available for training due to GPT-NeoX's requirement for a large-scale infrastructure. - If your project is limited by the Apache License terms or requires proprietary codebases without open-source contributions and modifications from external parties.
- 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++
- Is gpt-neox or ggml more popular on GitHub?
- ggml has more GitHub stars (15,185 vs 7,452). Stars measure visibility, not whether either tool fits your constraints.
- Are gpt-neox and ggml open source?
- Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, ggml: MIT).
- Where can I find alternatives to gpt-neox or ggml?
- GraphCanon lists graph-backed alternatives at gpt-neox alternatives and ggml alternatives (gpt-neox markdown twin, ggml 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, gpt-neox or ggml?
- gpt-neox: Steady. ggml: 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 gpt-neox and ggml?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-neox trust report; ggml trust report.