Praxis-1 Is Now Available

Contents

• State-of-the-Art Performance
• Adapt It to Your Hardware
• Why Open Weight
• Try it Today

Praxis-1, our state-of-the-art world action model, is now available to everyone with open weights under the Apache 2.0 license. We introduced Praxis-1 in September as our first world action model, built on the same large-scale video pretraining behind our general world models. Our launch partners including Noble Machines, Standard Bots and Ultra have been running it on their own hardware: a legged humanoid, a six-axis industrial arm and a dual-arm warehouse robot. Today, the same model is open to every robotics developer and researcher.

All videos at 1x speed.

State-of-the-Art Performance

Praxis-1 is a 27B-parameter mixture-of-experts diffusion transformer that predicts video and actions together. It is the top-performing world action model on NVIDIA's RoboLab-120 leaderboard, and it outperforms every open-weight VLA. On RoboLab-120, Praxis-1 reaches 46.2% success across 1,200 episodes, compared with 42.9% for FLUX 3 Action, 36.8% for Cosmos3 Nano and 28.0% for π0.5.

Praxis-1 is available today on GitHub, on Hugging Face with native LeRobot support, and in vLLM-Omni, with hosted inference on Reactor. Everything you need to run and adapt it on your own hardware is released under Apache 2.0, including for commercial use. That covers the weights and the inference and fine-tuning code, with support for both full fine-tuning and LoRA. Praxis-1 runs out of the box across NVIDIA hardware, from data center GPUs like H100 and B200, to workstation GPUs like RTX 5090 and RTX PRO 6000, to Jetson Thor on the robot itself.

All videos at 1x speed.

Adapt It to Your Hardware

Praxis-1 learns how objects behave and how tasks unfold from large-scale video, which gives it a head start on a new embodiment before fine-tuning on any hardware or domain-specific data. Our research has shown that web video matches teleoperated robot video as pretraining data for policy performance, which shifts the bottleneck on a capable policy to how much general video a model can learn from.

With our partners, a single Praxis-1 checkpoint has adapted to a variety of embodiments with light fine-tuning, and with LoRA you can adapt it to your own embodiment, cameras and tasks without retraining the full model. Fine-tuned on Google's RT-1 robot, which never appeared in its training data, Praxis-1 reaches 80.0% success on SimplerEnv, well above the original RT-1 policy at 52.4%.

Why Open Weight

When we announced Praxis-1, we committed to shipping it as an open model. U.S. leadership in physical AI will depend on investment in both hardware and software, and on a level of interoperability and openness from American models that physical use cases don't have yet. Open weights give hardware developers the flexibility to run, inspect and adapt a world action model on their own terms. Praxis-1 builds on Runway's work as a founding member of the Cosmos Coalition, advancing open world models for physical AI. Together, Runway and NVIDIA are opening frontier technology stacks giving developers shared foundations to build on and solve physical AI challenges.

Try it Today

Download Praxis-1 on Hugging Face and view the code on GitHub, or try it on Runway Dev. Developers can post train Praxis-1 for their domain specific use cases with NeMo Automodel hands-on tutorials. If you're planning a deployment, contact our robotics team.