---
title: "gpt-neox vs peft"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/eleutherai-gpt-neox-vs-huggingface-peft"
tools: ["eleutherai-gpt-neox", "huggingface-peft"]
---

# gpt-neox vs peft

*GraphCanon updated Aug 23, 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 peft if pEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python.

[gpt-neox](https://www.eleuther.ai/) reports 7.5k GitHub stars, 1.1k forks, and 111 open issues, last pushed Jun 11, 2026. [peft](https://huggingface.co/docs/peft) has 22k stars, 2.4k forks, and 74 open issues, last pushed Aug 22, 2026. Figures are from public GitHub metadata via [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox) and [peft's repository](https://github.com/huggingface/peft).

| | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [peft](/tools/huggingface-peft.md) |
| --- | --- | --- |
| Tagline | Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries | State-of-the-art Parameter-Efficient Fine-Tuning |
| Stars | 7,452 | 21,585 |
| Forks | 1,119 | 2,446 |
| Open issues | 111 | 74 |
| 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. | PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python. |
| 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 | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [peft](/tools/huggingface-peft.md) |
| --- | --- | --- |
| Maintenance | Steady (60%) | Very active (96%) |
| Days since push | 56d | 1d |
| Open issues (now) | 111 | 74 |
| Stars delta | Unknown | +142 (30d) |
| Open issues delta | Unknown | +16 (30d) |
| Full report | [trust report](/tools/eleutherai-gpt-neox/trust.md) | [trust report](/tools/huggingface-peft/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: peft

- **Adopt for:** PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python.

## 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, 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 peft if…

- Tags unique to peft: adapter, diffusion, fine-tuning, llm.
- When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting.
- More GitHub stars (22k vs 7.5k) - visibility, not fit.

## 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 peft

- If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only.
- When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.

## Common questions

### What is the difference between gpt-neox and peft?

gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. peft: State-of-the-art Parameter-Efficient Fine-Tuning. See the comparison table for live GitHub stats and shared categories.

### When should I choose gpt-neox over peft?

Choose gpt-neox over peft 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, 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 peft over gpt-neox?

Choose peft over gpt-neox when Tags unique to peft: adapter, diffusion, fine-tuning, llm; When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting; More GitHub stars (22k vs 7.5k) - visibility, not fit.

### 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 peft?

If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only. When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.

### Is gpt-neox or peft more popular on GitHub?

peft has more GitHub stars (21,585 vs 7,452). Stars measure visibility, not whether either tool fits your constraints.

### Are gpt-neox and peft open source?

Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, peft: Apache-2.0).

### Where can I find alternatives to gpt-neox or peft?

GraphCanon lists graph-backed alternatives at [gpt-neox alternatives](/tools/eleutherai-gpt-neox/alternatives) and [peft alternatives](/tools/huggingface-peft/alternatives) ([gpt-neox markdown twin](/tools/eleutherai-gpt-neox/alternatives.md), [peft markdown twin](/tools/huggingface-peft/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-peft.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, gpt-neox or peft?

gpt-neox: Steady. peft: 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 peft?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gpt-neox trust report](/tools/eleutherai-gpt-neox/trust); [peft trust report](/tools/huggingface-peft/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/_