--- title: "gpt-neox vs femtoGPT" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eleutherai-gpt-neox-vs-keyvank-femtogpt" tools: ["eleutherai-gpt-neox", "keyvank-femtogpt"] --- # gpt-neox vs femtoGPT *GraphCanon updated Aug 8, 2026* ## 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. [gpt-neox](https://www.eleuther.ai/) reports 7.5k GitHub stars, 1.1k forks, and 111 open issues, last pushed Jun 11, 2026. [femtoGPT](https://discord.gg/wTJFaDVn45) has 935 stars, 67 forks, and 10 open issues, last pushed Oct 21, 2025. Figures are from public GitHub metadata via [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox) and [femtoGPT's repository](https://github.com/keyvank/femtoGPT). | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [femtoGPT](/tools/keyvank-femtogpt.md) | | --- | --- | --- | | Tagline | Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries | Pure Rust implementation of a minimal Generative Pretrained Transformer | | Stars | 7,452 | 935 | | Forks | 1,119 | 67 | | Open issues | 111 | 10 | | Language | Python | Rust | | Adopt for | 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. | A minimalistic GPT-style language model framework in Rust, suitable for both CPU and GPU inference and training via OpenCL. | | Persona | - | developer harness | | Runtime | - | - | | License | 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 | MIT License, permitting any use as long as all copyright and license information are retained. | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [femtoGPT](/tools/keyvank-femtogpt.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 56d | 290d | | Open issues (now) | 111 | 10 | | Owner type | Organization | User | | Full report | [trust report](/tools/eleutherai-gpt-neox/trust.md) | [trust report](/tools/keyvank-femtogpt/trust.md) | ## Decision facts: gpt-neox - **Pricing:** freemium - Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations. - **Adopt for:** 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. - **License detail:** 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 ## Decision facts: femtoGPT - **Requirements:** Requires the Rust toolchain installed on your system.; If targeting GPU usage, correct installation of GPU drivers along with OpenCL runtimes is necessary. - **Adopt for:** A minimalistic GPT-style language model framework in Rust, suitable for both CPU and GPU inference and training via OpenCL. - **License detail:** MIT License, permitting any use as long as all copyright and license information are retained. - **Persona:** developer harness ## Choose when ### 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. ### 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 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 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. ## 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](/tools/eleutherai-gpt-neox/alternatives) and [femtoGPT alternatives](/tools/keyvank-femtogpt/alternatives) ([gpt-neox markdown twin](/tools/eleutherai-gpt-neox/alternatives.md), [femtoGPT markdown twin](/tools/keyvank-femtogpt/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/eleutherai-gpt-neox-vs-keyvank-femtogpt.md) 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](/tools/eleutherai-gpt-neox/trust); [femtoGPT trust report](/tools/keyvank-femtogpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eleutherai-gpt-neox`](/api/graphcanon/graph?tool=eleutherai-gpt-neox) - 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/_