Comparison
gpt-neox vs femtoGPT
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 femtoGPT if a minimalistic GPT-style language model framework in Rust, suitable for both CPU and GPU inference and training via OpenCL.
Markdown twin · gpt-neox alternatives · femtoGPT alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | gpt-neox | femtoGPT |
|---|---|---|
| Maintenance | Steady (56d since push) As of 2w · github_public_v1 | Slowing (290d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- gpt-neox
- Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries
- femtoGPT
- Pure Rust implementation of a minimal Generative Pretrained Transformer
Stars
- gpt-neox
- 7.5k
- femtoGPT
- 935
Forks
- gpt-neox
- 1.1k
- femtoGPT
- 67
Open issues
- gpt-neox
- 111
- femtoGPT
- 10
Language
- gpt-neox
- Python
- femtoGPT
- Rust
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.
- femtoGPT
- A minimalistic GPT-style language model framework in Rust, suitable for both CPU and GPU inference and training via OpenCL.
Persona
- gpt-neox
- -
- femtoGPT
- developer harness
Runtime
- gpt-neox
- -
- femtoGPT
- -
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
- femtoGPT
- MIT License, permitting any use as long as all copyright and license information are retained.
Last pushed
- gpt-neox
- Jun 11, 2026
- femtoGPT
- Oct 21, 2025
Categories
- gpt-neox
- LLM Frameworks, Model Training
- femtoGPT
- LLM Frameworks, Model Training
Trust and health
Maintenance
- gpt-neox
- Steady (60%)
- femtoGPT
- Slowing (36%)
Days since push
- gpt-neox
- 56d
- femtoGPT
- 290d
Open issues (now)
- gpt-neox
- 111
- femtoGPT
- 10
Owner type
- gpt-neox
- Organization
- femtoGPT
- User
Full report
- gpt-neox
- Trust report
- femtoGPT
- Trust report
Choose gpt-neox if…
- gpt-neox is primarily Python; femtoGPT is Rust.
- License: gpt-neox is Apache-2.0, femtoGPT 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.
- - 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 femtoGPT if…
- femtoGPT is primarily Rust; gpt-neox is Python.
- License: femtoGPT is MIT, gpt-neox is Apache-2.0.
- Requirements: Requires the Rust toolchain installed on your system.; If targeting GPU usage, correct installation of GPU drivers along with OpenCL runtimes is necessary..
- Tags unique to femtoGPT: from-scratch, gpt, gpu, machine-learning.
- When you want a pure Rust implementation that provides an easy-to-understand basis for learning about the inner workings of AI models.
When NOT to use femtoGPT
- When high performance is required as femtoGPT operates relatively slower compared to optimized models, especially for large-scale training.
- If your project strictly needs CUDA-based optimization specific to NVIDIA GPUs, given that femtoGPT leverages OpenCL for GPU support.
- In cases where the project demands a fully tested and production-ready model; femtoGPT's architecture correctness is not guaranteed due to possible implementation errors.
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 (keyvank/femtoGPT) · observed Aug 8, 2026
- GitHub forks (keyvank/femtoGPT) · observed Aug 8, 2026
- Last push (keyvank/femtoGPT) · observed Oct 21, 2025
- License file (MIT) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: gpt-neox 7.5k · femtoGPT 935 (synced Aug 7, 2026).
Common questions
- What is the difference between gpt-neox and femtoGPT?
- gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. femtoGPT: Pure Rust implementation of a minimal Generative Pretrained Transformer. See the comparison table for live GitHub stats and shared categories.
- When should I choose gpt-neox over femtoGPT?
- Choose gpt-neox over femtoGPT when gpt-neox is primarily Python; femtoGPT is Rust; License: gpt-neox is Apache-2.0, femtoGPT 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; - 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 femtoGPT over gpt-neox?
- Choose femtoGPT over gpt-neox when femtoGPT is primarily Rust; gpt-neox is Python; License: femtoGPT is MIT, gpt-neox is Apache-2.0; Requirements: Requires the Rust toolchain installed on your system.; If targeting GPU usage, correct installation of GPU drivers along with OpenCL runtimes is necessary.; Tags unique to femtoGPT: from-scratch, gpt, gpu, machine-learning; When you want a pure Rust implementation that provides an easy-to-understand basis for learning about the inner workings of AI models.
- 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 femtoGPT?
- When high performance is required as femtoGPT operates relatively slower compared to optimized models, especially for large-scale training. If your project strictly needs CUDA-based optimization specific to NVIDIA GPUs, given that femtoGPT leverages OpenCL for GPU support. In cases where the project demands a fully tested and production-ready model; femtoGPT's architecture correctness is not guaranteed due to possible implementation errors.
- Is gpt-neox or femtoGPT more popular on GitHub?
- gpt-neox has more GitHub stars (7,452 vs 935). Stars measure visibility, not whether either tool fits your constraints.
- Are gpt-neox and femtoGPT open source?
- Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, femtoGPT: MIT).
- Where can I find alternatives to gpt-neox or femtoGPT?
- GraphCanon lists graph-backed alternatives at gpt-neox alternatives and femtoGPT alternatives (gpt-neox markdown twin, femtoGPT 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 femtoGPT?
- gpt-neox: Steady. femtoGPT: Slowing. 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 femtoGPT?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-neox trust report; femtoGPT trust report.