--- title: "gpt-neox vs optimum-tpu" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eleutherai-gpt-neox-vs-huggingface-optimum-tpu" tools: ["eleutherai-gpt-neox", "huggingface-optimum-tpu"] --- # gpt-neox vs optimum-tpu *GraphCanon updated Aug 7, 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 optimum-tpu if optimum-tpu is tailored for Python developers working with transformers models aiming to leverage the power of Google TPUs. [gpt-neox](https://www.eleuther.ai/) reports 7.5k GitHub stars, 1.1k forks, and 111 open issues, last pushed Jun 11, 2026. [optimum-tpu](https://huggingface.co/docs/optimum-tpu) has 135 stars, 30 forks, and 4 open issues, last pushed Jan 23, 2026. Figures are from public GitHub metadata via [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox) and [optimum-tpu's repository](https://github.com/huggingface/optimum-tpu). | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [optimum-tpu](/tools/huggingface-optimum-tpu.md) | | --- | --- | --- | | Tagline | Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries | Google TPU optimizations for transformers models | | Stars | 7,452 | 135 | | Forks | 1,119 | 30 | | Open issues | 111 | 4 | | Language | Python | Python | | 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. | optimum-tpu is tailored for Python developers working with transformers models aiming to leverage the power of Google TPUs. | | Persona | - | - | | 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 | Apache-2.0 | | Categories | LLM Frameworks, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [optimum-tpu](/tools/huggingface-optimum-tpu.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Archived (8%) | | Days since push | 56d | 193d | | Archived on GitHub | No | Yes | | Open issues (now) | 111 | 4 | | Full report | [trust report](/tools/eleutherai-gpt-neox/trust.md) | [trust report](/tools/huggingface-optimum-tpu/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: optimum-tpu - **Hosting:** self hosted - **Pricing:** freemium - **Adopt for:** optimum-tpu is tailored for Python developers working with transformers models aiming to leverage the power of Google TPUs. - **License detail:** Apache-2.0 ## Choose when ### Choose gpt-neox if… - 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. - 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. ### Choose optimum-tpu if… - Tags unique to optimum-tpu: optimizations, tpu. - Use optimum-tpu when you require high performance execution of transformers models on Google TPUs, as it offers specific optimizations for that hardware. - Leaner open-issue backlog (4). ## 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 optimum-tpu - Avoid using optimum-tpu if your infrastructure does not include or will not support Google TPUs, since its optimizations are not beneficial on other hardware. - Skip this tool if you are working in environments with strict licensing requirements as it requires adherence to the Apache-2.0 license. ## Common questions ### What is the difference between gpt-neox and optimum-tpu? gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. optimum-tpu: Google TPU optimizations for transformers models. See the comparison table for live GitHub stats and shared categories. ### When should I choose gpt-neox over optimum-tpu? Choose gpt-neox over optimum-tpu when 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; 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 optimum-tpu over gpt-neox? Choose optimum-tpu over gpt-neox when Tags unique to optimum-tpu: optimizations, tpu; Use optimum-tpu when you require high performance execution of transformers models on Google TPUs, as it offers specific optimizations for that hardware; Leaner open-issue backlog (4). ### 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 optimum-tpu? Avoid using optimum-tpu if your infrastructure does not include or will not support Google TPUs, since its optimizations are not beneficial on other hardware. Skip this tool if you are working in environments with strict licensing requirements as it requires adherence to the Apache-2.0 license. ### Is gpt-neox or optimum-tpu more popular on GitHub? gpt-neox has more GitHub stars (7,452 vs 135). Stars measure visibility, not whether either tool fits your constraints. ### Are gpt-neox and optimum-tpu open source? Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, optimum-tpu: Apache-2.0). ### Where can I find alternatives to gpt-neox or optimum-tpu? GraphCanon lists graph-backed alternatives at [gpt-neox alternatives](/tools/eleutherai-gpt-neox/alternatives) and [optimum-tpu alternatives](/tools/huggingface-optimum-tpu/alternatives) ([gpt-neox markdown twin](/tools/eleutherai-gpt-neox/alternatives.md), [optimum-tpu markdown twin](/tools/huggingface-optimum-tpu/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-huggingface-optimum-tpu.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gpt-neox or optimum-tpu? gpt-neox: Steady. optimum-tpu: Archived. 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 optimum-tpu? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gpt-neox trust report](/tools/eleutherai-gpt-neox/trust); [optimum-tpu trust report](/tools/huggingface-optimum-tpu/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/_