# NVIDIA Corporation — developer ecosystem report

- GitHub org: `NVIDIA`
- Category: ai
- Overall score: 61.0 / 100
- Overall rank: #59
- Last refreshed: 2026-08-13

## Pillar scores

| Pillar | Score / 100 | Rank | Weight in overall |
| --- | --- | --- | --- |
| Code & Polish | 66.0 | #150 | ~22% |
| Education | 81.0 | #86 | ~18% |
| AI readiness | 60.0 | #84 | part of Reach |
| Builder experience | 0.0 | #1 | ~12% |
| Community | 57.0 | #62 | ~16% |
| Reach | 44.0 | #431 | ~18% |
| Momentum | 55.0 | #168 | ~14% |

## Links

- GitHub: https://github.com/NVIDIA
- Website: https://nvidia.com
- Docs: https://docs.nvidia.com

## Public GitHub totals

| Metric | Value |
| --- | --- |
| Repos analyzed | 12 |
| Stars | 273,298 |
| Forks | 42,827 |
| Open issues | 16,908 |
| Contributors | — |

## Top repositories

| Repo | Stars | Language | Description |
| --- | --- | --- | --- |
| [NemoClaw](https://github.com/NVIDIA/NemoClaw) | 22,148 | TypeScript | Run agents like Hermes, LangChain Deep Agents, and OpenClaw more securely inside NVIDIA OpenShell with managed inference |
| [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) | 17,589 | — | Build and run Docker containers leveraging NVIDIA GPUs |
| [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) | 17,420 | Python | Ongoing research training transformer models at scale |
| [open-gpu-kernel-modules](https://github.com/NVIDIA/open-gpu-kernel-modules) | 17,283 | C | NVIDIA Linux open GPU kernel module source |
| [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) | 14,846 | Jupyter Notebook | State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and per |
| [SkillSpector](https://github.com/NVIDIA/SkillSpector) | 14,596 | Python | Security scanner for AI agent skills. Detect vulnerabilities, malicious patterns, security risks, prompt injection, data |
| [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) | 14,377 | Python | TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of- |
| [cosmos](https://github.com/NVIDIA/cosmos) | 11,488 | Jupyter Notebook | NVIDIA Cosmos is an open platform of world models, datasets, and tools that enables developers to build Physical AI for  |
| [personaplex](https://github.com/NVIDIA/personaplex) | 10,306 | Python | PersonaPlex code. |
| [cutlass](https://github.com/NVIDIA/cutlass) | 10,245 | C++ | CUDA Templates and Python DSLs for High-Performance Linear Algebra |
| [cudf](https://github.com/NVIDIA/cudf) | 9,728 | C++ | cuDF - GPU DataFrame Library  |
| [garak](https://github.com/NVIDIA/garak) | 8,792 | Python | the LLM vulnerability scanner |

## Methodology

Scores are computed from public signals only (GitHub, package registries, docs sites, community platforms). Pillar definitions: https://www.smoower.com/docs.md

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