Open Source
GPT-NeoX
An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
Our verdict
We have not reviewed GPT-NeoX yet
No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.
From the listing
About GPT-NeoX
GPT-NeoX is a framework for training large-scale autoregressive transformer models. It implements model parallelism across GPUs using Megatron and DeepSpeed libraries. Built by EleutherAI, it is designed for researchers to train GPT-like models at scale.
Best for
Researchers and engineers training custom large language models
Use cases
- Training large language models from scratch
- Experimenting with model parallelism techniques
- Fine-tuning autoregressive transformers on custom datasets
Notes
7,432 stars on GitHub. Last updated 2026-05-19. Licensed Apache-2.0.
Pros
- Enables training of very large models (tens of billions of parameters)
- Leverages proven Megatron and DeepSpeed optimizations
- Open source with strong community support (over 7,000 stars)
Cons
- Requires substantial GPU compute infrastructure
- Primarily suited for autoregressive models only
- Less polished than commercial offerings; may require deep engineering expertise
Key facts
What we have checked
- Latest version
- 2.0 (released 10 Mar 2023)
- GitHub stars
- 7,464
Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.
Stars and usage
How GPT-NeoX is growing
7,432 to 7,464
Sources
Where this comes from
- GitHubOfficial
- github api via nightly crawl, recovered from git history 20e874270887c1008f860ee0198e461a23c9e438Registry
A source shows what was published when we checked it. It does not certify security, performance or a working installation. Read our method.
Changelog
What changed
Released 2.0
Released 1.0
Released legacy_gptj_residual.1.0.0
Pairs with
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