Deploy AI to constrained edge devices.
Smart camera teams, retail analytics engineers, medical imaging groups. Kronos takes your vision model from PyTorch to a pre-built binary on Jetson Orin Nano, Orin NX, and AGX Orin. No on-device compilation. No PyTorch runtime. No SDK dependency. Ship to the entire fleet in days, not months.
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Built for teams shipping AI to power-constrained devices.
Edge devices run on watts, not kilowatts. Kronos compiles your model for the exact power envelope of each target. No runtime bloat. No SDK management. One binary per device, optimized and ready to flash.
Compiled for power-constrained silicon.
From 5W to 60W. Orin Nano, Orin NX, AGX Orin. Kronos generates binaries optimized for the thermal and power envelope of each target SoC. The same source model, the right binary for each device class.
No runtime on device.
Static binary execution. No PyTorch, no ONNX Runtime, no SDK dependency at inference time. The binary contains everything it needs. Smaller attack surface, deterministic behavior, no internet dependency in the field.
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Same model. Every power envelope.
Kronos compiles your model for the Orin Nano at 15W, the Orin NX at 25W, and the AGX Orin at 60W. Each binary is optimized for the thermal constraints of its target. One source model, three device classes, three power-tuned binaries.
Detection. Classification. Segmentation. Compiled natively.
Object detection, image classification, semantic segmentation — with your custom pre- and post-processing baked in. Kronos compiles the full vision pipeline end-to-end. No CPU fallbacks. No operator gaps. Every layer runs on the accelerator.
Pre-built images. Push to fleet.
Kronos produces binary images ready for device provisioning. No on-device compilation. No internet required at the edge. Flash the image, deploy. Works with existing fleet management and OTA update pipelines. Scale from ten devices to ten thousand.
From per-device SDK chaos to a single compile.
An edge AI design partner deploys vision models across retail analytics cameras and industrial inspection devices on multiple Jetson targets. Kronos replaced their per-device conversion pipeline.
"We used to maintain three conversion pipelines for three device SKUs. Now we maintain zero. The model team ships a checkpoint, Kronos handles the rest. Devices get flashed, inference starts."
Challenge
"Each device target required a separate model conversion pipeline. Orin Nano needed aggressive quantization to fit the power budget. Orin NX allowed more headroom but demanded different TensorRT profiles. Every JetPack update broke something. The team spent more time managing SDKs than improving models."
Solution
Kronos compiled the same source model for every device target in the fleet. Power-aware optimization handled the 15W and 25W constraints automatically. Pre-built binaries eliminated on-device compilation entirely. New device targets compile in minutes, not weeks. SDK version management disappeared.
Products used
Kronos · yasp.agent · yasp.codegen · KernelDB
Real numbers, at real scale.
<12ms
Inference latency on Orin Nano at 15W
3
Device targets from one source model
40%
Power savings vs. PyTorch + TensorRT
100%
Native compilation, zero CPU fallbacks
Your model. Run it on every device.
See Kronos compile your vision model for the edge devices you ship today, optimized for every power envelope in your fleet.
Ship with Kronos.
Scale with Gaia.
Kronos takes your model from research to a self-contained binary on Nvidia silicon. Gaia delivers maximum throughput across Nvidia, AMD, and AWS Trainium. Same agentic platform under the hood.
Kronos
Path to Nvidia production
PyTorch model in, self-contained binary out. Compiles for Jetson Orin, Drive AGX, H100, H200, and other Nvidia silicon. Custom modules compile natively. Weeks of deployment work collapse into a single guided pipeline.
