Praxis-1: A Developer's Checklist Before You Run an Open Robot Model on Your Hardware

Before deploying an open robot model like Praxis-1 on real hardware, developers should follow a checklist to ensure safe and effective operation. First, select the appropriate checkpoint checkpoint, either the base mid-trained model, one fine-tuned on DROID, or one fine-tuned for the bimanual YAM robot. The DROID fine-tune is recommended for single-arm tabletop work.

Second, accurately size the GPU for the model. The full bf16 weights require a single 80 GB GPU, but the NVFP4 4-bit version is 68% smaller and achieves similar performance. Third, estimate the cost of running the model, which is approximately $0.12 per 10-minute task. Fourth, properly bind the hardware before trusting the model's policy.

The open weights do not guarantee plug-and-play motion, so the model's format must be mapped to the hardware's expected format, and local demonstrations may be necessary for fine-tuning. Fifth, design for the model's performance of 46.2%, rather than aiming for 100%. Build a loop to attempt, verify, and retry or re-plan tasks. Sixth, verify the checkpoint like code, as it has the potential to move hardware.

Check the publisher, checkpoint hash, and version, diff against a known-good baseline, audit the checkpoint, and simulate before running unsupervised on a real robot. Lastly, test the model on your specific embodiment and tasks, not just the leaderboard results. The key takeaway is that a competitive robot foundation model is now a downloadable asset, and success depends on the quality of demonstrations, a safe loop, and reliable hardware integration.