---
title: "gpt-neox vs long-context-attention"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/eleutherai-gpt-neox-vs-feifeibear-long-context-attention"
tools: ["eleutherai-gpt-neox", "feifeibear-long-context-attention"]
---

# gpt-neox vs long-context-attention

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick gpt-neox if gPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries; pick long-context-attention if long-context-attention uses Unified Sequence Parallel Attention techniques to improve performance of long context transformers for both training and inference.

[gpt-neox](https://www.eleuther.ai/) reports 7.5k GitHub stars, 1.1k forks, and 118 open issues, last pushed Sep 4, 2026. [long-context-attention](https://github.com/feifeibear/long-context-attention) has 693 stars, 85 forks, and 13 open issues, last pushed Sep 16, 2026. Figures are from public GitHub metadata via [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox) and [long-context-attention's repository](https://github.com/feifeibear/long-context-attention).

| | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [long-context-attention](/tools/feifeibear-long-context-attention.md) |
| --- | --- | --- |
| Tagline | Model parallel autoregressive transformers on GPUs | Unified Sequence Parallel Attention for Long Context Transformers |
| Stars | 7,461 | 693 |
| Forks | 1,121 | 85 |
| Open issues | 118 | 13 |
| Language | Python | Python |
| Adopt for | GPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries. | long-context-attention uses Unified Sequence Parallel Attention techniques to improve performance of long context transformers for both training and inference. |
| Persona | - | - |
| Runtime | - | - |
| License | GPT-NeoX is licensed under the Apache License, Version 2.0, which allows for free use, modification, and distribution of the software, provided that the license terms are adhered to. | Apache-2.0 |
| Categories | LLM Frameworks, Model Training | Inference & Serving, Model Training |

## Trust and health

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

| | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [long-context-attention](/tools/feifeibear-long-context-attention.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Very active (96%) |
| Days since push | 13d | 3d |
| Open issues (now) | 118 | 13 |
| Stars delta | +9 (30d) | +11 (30d) |
| Open issues delta | +7 (30d) | 0 (30d) |
| Owner type | Organization | User |
| Full report | [trust report](/tools/eleutherai-gpt-neox/trust.md) | [trust report](/tools/feifeibear-long-context-attention/trust.md) |

## Decision facts: gpt-neox

- **Requirements:** Min 16 GB RAM; Requires GPU support for optimal performance.; Integration with Megatron and DeepSpeed libraries is essential for full functionality.
- **Adopt for:** GPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries.
- **License detail:** GPT-NeoX is licensed under the Apache License, Version 2.0, which allows for free use, modification, and distribution of the software, provided that the license terms are adhered to.

## Decision facts: long-context-attention

- **Adopt for:** long-context-attention uses Unified Sequence Parallel Attention techniques to improve performance of long context transformers for both training and inference.

## Choose when

### Choose gpt-neox if…

- Requirements: Min 16 GB RAM; Requires GPU support for optimal performance.; Integration with Megatron and DeepSpeed libraries is essential for full functionality..
- Tags unique to gpt-neox: autoregressive-transformers, deepspeed-library, gpt-3, gpu.
- Also covers LLM Frameworks.
- When you need to train large language models on GPUs and require model parallelism to handle the computational demands.

### Choose long-context-attention if…

- Tags unique to long-context-attention: attention-is-all-you-need, deepspeed-ulysses, llm-inference, llm-training.
- Also covers Inference & Serving.
- When developing models that require handling longer input sequences where traditional attention mechanisms face scalability issues.

## When NOT to use gpt-neox

- If your project does not require the scale of model parallelism and can be handled with single-GPU setups, GPT-NeoX might introduce unnecessary complexity.
- When the specific features of Megatron and DeepSpeed are not aligned with your project's needs, opting for a simpler framework might be more suitable.

## When NOT to use long-context-attention

- If your use case involves short context lengths where standard attention mechanisms suffice and adding long-context-attention doesn't provide significant benefits.
- When working in environments that do not support Python, as this tool is specifically developed for the Python ecosystem.

## Common questions

### What is the difference between gpt-neox and long-context-attention?

gpt-neox: Model parallel autoregressive transformers on GPUs. long-context-attention: Unified Sequence Parallel Attention for Long Context Transformers. See the comparison table for live GitHub stats and shared categories.

### When should I choose gpt-neox over long-context-attention?

Choose gpt-neox over long-context-attention when Requirements: Min 16 GB RAM; Requires GPU support for optimal performance.; Integration with Megatron and DeepSpeed libraries is essential for full functionality.; Tags unique to gpt-neox: autoregressive-transformers, deepspeed-library, gpt-3, gpu; Also covers LLM Frameworks; When you need to train large language models on GPUs and require model parallelism to handle the computational demands.

### When should I choose long-context-attention over gpt-neox?

Choose long-context-attention over gpt-neox when Tags unique to long-context-attention: attention-is-all-you-need, deepspeed-ulysses, llm-inference, llm-training; Also covers Inference & Serving; When developing models that require handling longer input sequences where traditional attention mechanisms face scalability issues.

### When should I avoid gpt-neox?

If your project does not require the scale of model parallelism and can be handled with single-GPU setups, GPT-NeoX might introduce unnecessary complexity. When the specific features of Megatron and DeepSpeed are not aligned with your project's needs, opting for a simpler framework might be more suitable.

### When should I avoid long-context-attention?

If your use case involves short context lengths where standard attention mechanisms suffice and adding long-context-attention doesn't provide significant benefits. When working in environments that do not support Python, as this tool is specifically developed for the Python ecosystem.

### Is gpt-neox or long-context-attention more popular on GitHub?

gpt-neox has more GitHub stars (7,461 vs 693). Stars measure visibility, not whether either tool fits your constraints.

### Are gpt-neox and long-context-attention open source?

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

### Where can I find alternatives to gpt-neox or long-context-attention?

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

### Which is better maintained, gpt-neox or long-context-attention?

gpt-neox: Active. long-context-attention: 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 long-context-attention?

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