Comparison
gpt-neox vs long-context-attention
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.
Markdown twin · gpt-neox alternatives · long-context-attention alternatives
GraphCanon updated Sep 20, 2026
5views this month
vs
Trust & integrity
| Signal | gpt-neox | long-context-attention |
|---|---|---|
| Maintenance | Active (13d since push) As of Sep 18, 2026 · github_public_v1 | Very active (3d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- Model parallel autoregressive transformers on GPUs
- long-context-attention
- Unified Sequence Parallel Attention for Long Context Transformers
Stars
- gpt-neox
- 7.5k
- long-context-attention
- 693
Forks
- gpt-neox
- 1.1k
- long-context-attention
- 85
Open issues
- gpt-neox
- 118
- long-context-attention
- 13
Language
- gpt-neox
- Python
- long-context-attention
- Python
Adopt for
- gpt-neox
- GPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries.
- long-context-attention
- long-context-attention uses Unified Sequence Parallel Attention techniques to improve performance of long context transformers for both training and inference.
Persona
- gpt-neox
- -
- long-context-attention
- -
Runtime
- gpt-neox
- -
- long-context-attention
- -
License
- gpt-neox
- 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.
- long-context-attention
- Apache-2.0
Last pushed
- gpt-neox
- Sep 4, 2026
- long-context-attention
- Sep 16, 2026
Categories
- gpt-neox
- LLM Frameworks, Model Training
- long-context-attention
- Inference & Serving, Model Training
Trust and health
Maintenance
- gpt-neox
- Active (82%)
- long-context-attention
- Very active (96%)
Days since push
- gpt-neox
- 13d
- long-context-attention
- 3d
Open issues (now)
- gpt-neox
- 118
- long-context-attention
- 13
Stars delta
- gpt-neox
- +9 (30d)
- long-context-attention
- +11 (30d)
Open issues delta
- gpt-neox
- +7 (30d)
- long-context-attention
- 0 (30d)
Owner type
- gpt-neox
- Organization
- long-context-attention
- User
Full report
- gpt-neox
- Trust report
- long-context-attention
- Trust report
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.
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.
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 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.
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 Sep 20, 2026
- GitHub forks (EleutherAI/gpt-neox) · observed Sep 20, 2026
- Last push (EleutherAI/gpt-neox) · observed Sep 4, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (feifeibear/long-context-attention) · observed Sep 20, 2026
- GitHub forks (feifeibear/long-context-attention) · observed Sep 20, 2026
- Last push (feifeibear/long-context-attention) · observed Sep 16, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: gpt-neox 7.5k · long-context-attention 693 (synced Sep 20, 2026).
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 and long-context-attention alternatives (gpt-neox markdown twin, long-context-attention 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 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; long-context-attention trust report.