NVIDIA Research Projects
@nvlabs • Open source projects from nvlabs. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
64%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
30 repos
Visible snapshot
27 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (25), Unknown (2), TypeScript (1)
Average size
270
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-10-07(2d ago)GitHub API fallback30 repositories
64%
of the visible star count comes from this organization's top three repositories.
270
stars per repository in this same snapshot.
Python
is the most common language here, with 27 repositories updated in the last 90 days.
Why this rank
This organization stands out because its public portfolio is relatively balanced across 30 repositories.
Balanced portfolio across 30 reposTop 3 share 64%
Organization pages work best when you separate portfolio breadth from flagship concentration. In NVIDIA Research Projects's case, the visible top three repositories account for about 64% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Python (25), Unknown (2), TypeScript (1). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | nvlabs/SoL-Pi SoL-Pi: Scaling Auto-Research Loops for Efficient Agent Harnesses | TypeScript | 3.4K |
| 2 | nvlabs/kda Kernel Design Agents (KDA) is a agent-centric workflow to write high-performance CUDA Kernels. | 1.3K | |
| 3 | nvlabs/SpatialClaw [NeurIPS 2026] SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning | Python | 433 |
| 4 | nvlabs/SimFoundry Modular and Automated Scene Generation for Policy Learning and Evaluation | Python | 414 |
| 5 | nvlabs/CuTe Reference implementation and examples of the CuTe Layout representation and algebra. | Python | 368 |
| 6 | nvlabs/GatedDeltaNet-2 Official PyTorch Implementation of Gated DeltaNet-2: Decoupling Erase and Write in Linear Attention | Python | 328 |
| 7 | nvlabs/alpamayo2 NVIDIA Alpamayo 2 Super is an open 34B multi-task foundation model designed to supercharge autonomous vehicle development. | Python | 270 |
| 8 | nvlabs/ENPIRE ENPIRE is an agentic framework that allows AI coding agents to autonomously self-improve real-world robot policies through closed-loop physical trial, error, and evolution | Python | 269 |
| 9 | nvlabs/ASPIRE ASPIRE: Agentic /Skills Discovery for Robotics | Python | 258 |
| 10 | nvlabs/Skill2Env Reinforcing Agents with Collective Skills | Python | 167 |
| 11 | nvlabs/alpagym AlpaGym is a reinforcement-learning framework for end-to-end autonomous-driving policies. | Python | 157 |
| 12 | nvlabs/alpamayo-coc-autolabeler NVIDIA CoC Autolabeler is an open autolabeling pipeline for autonomous vehicles that generates Chain-of-Causation reasoning labels and meta-actions from raw driving clips. | Python | 132 |
| 13 | nvlabs/Nemotron-Labs-Diffusion | Python | 94 |
| 14 | nvlabs/SparDA Sparse Decoupled Attention for Efficient Long-Context LLM Inference | Python | 74 |
| 15 | nvlabs/scal3r Prompt tune geometry reconstruction models for long-sequence. | Python | 69 |
| 16 | nvlabs/VoLoAgent | Python | 58 |
| 17 | nvlabs/neuralappearance | Python | 49 |
| 18 | nvlabs/DroidPlus Simple Droid platform stack | Python | 44 |
| 19 | nvlabs/ACE-RTL An agentic context evolution approach for RTL coding | Python | 36 |
| 20 | nvlabs/falcor2 | C++ | 32 |
| 21 | nvlabs/RoboVoLo | Python | 31 |
| 22 | nvlabs/DVSM Decoder-only View Synthesis Model | 18 | |
| 23 | nvlabs/humanoidmimicgen Official loco-manipulation simulation benchmark environments from the HumanoidMimicGen project. | Python | 15 |
| 24 | nvlabs/dexplore | Python | 12 |
| 25 | nvlabs/g2lf Real-time 3D Visualization of Radiance Fields on Light Field Displays | C | 7 |
| 26 | nvlabs/HealDA Direct observation-to-state data assimilation for the atmosphere | Python | 3 |
| 27 | nvlabs/EDAFlowQA EDAFlowQA | Python | 3 |
| 28 | nvlabs/Accelerated_TN_PTSBE Accelerated Tensor Network using PTSBE | Python | 3 |
| 29 | nvlabs/Schemacoder Schemacoder | Python | 3 |
| 30 | nvlabs/falcor2-data Data submodule for falcor2 | Python | 0 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.