Train on your data.
Use robot demonstrations, device logs, or simulation environments for supervised fine-tuning and reinforcement learning in GPU sandboxes.
AI for the
physical world.
Train on your data and deploy with World Model Runtime (WMR). Run on-device, on local compute, or in-region, with models tuned to your hardware and latency targets.
Run larger models on
your edge hardware.
WMR optimizes supported vision-language-action (VLA) and world models to reduce latency and fit larger models within your device’s memory and power limits.
Jetson Orin and Qualcomm
Builds tuned with our team for your model and device.
Custom hardware
Work with us on the runtime integration and optimization your platform needs.
Train on the data
your devices see.
Bring your training loop or work with our research team. Scale training and simulation on Hedra GPU clusters.
Close the loop.
Deploy supported checkpoints directly to WMR, without a separate conversion step. Feed new device data into your next training run.
Process multiple sensor streams at once.
WMR handles live video and sensor inputs, with redundancy and fallback at the compute layer.
Choose where to add compute.
Run on-device with no network in the inference path. Use a Hedra-managed local rack for larger models, with in-region cloud fallback where connectivity allows.
Protect the model on the device.
Hedra Sandboxes and WMR encrypt weights, proprietary code, and sensor data from training through edge deployment.