Your full perception stack, compiled for every vehicle.
Autonomous driving teams use Kronos to compile entire model stacks — perception, prediction, planning — into production binaries for NVIDIA Drive AGX and Jetson. What used to be weeks of conversion work per hardware target, per model, per JetPack version, becomes a single guided pipeline across the fleet.
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Built for teams shipping AI to autonomous fleets.
The path from research model to every vehicle in the fleet, automated. Your ML engineers stay focused on perception and planning. Kronos handles compilation, hardware targeting, and the path to a deterministic binary across every vehicle variant..
Full model stacks, one pipeline.
Perception, prediction, planning. Three models, dozens of custom modules, one compilation run. Kronos takes the full stack from PyTorch to production binaries for Drive AGX Orin, Drive AGX Thor, and Jetson. No model-by-model conversion. No per-target rework.
Safety-grade binaries. No runtime dependencies.
Self-contained, deterministic execution. No PyTorch in the vehicle. No SDK version drift across the fleet. No cloud dependency in the inference path. Static binaries you can audit, validate, and push to production with confidence.
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One Source.
Every vehicle in the fleet.
Kronos compiles your models for Drive AGX Orin, Drive AGX Thor, Jetson AGX, and other NVIDIA silicon from the same source. Different hardware generations across the fleet, same model code. Upgrade a chip variant or roll out a new platform — recompile, validate, push.
Camera-LiDAR-radar fusion. Compiled natively.
Custom fusion layers, cross-attention modules, BEV transforms, learned post-processing. Kronos generates kernels for every operator in the sensor fusion pipeline that vendor SDKs can't handle. No fallback to CPU in the safety-critical path. No dropped layers. Your full graph, end-to-end.
Compiled binaries.
Push to fleet.
From months of rework to same-week deployment
An autonomous trucking design partner uses Kronos to compile full perception stacks for production across multiple NVIDIA targets, with custom sensor fusion modules compiled natively.
"We used to budget six weeks every time we targeted a new platform. Now the whole stack compiles in an afternoon and we spend that time on the models instead of the deployment."
Challenge
"Every new hardware target meant weeks of rework: TensorRT conversion, unsupported operator workarounds, JetPack version management, and manual validation for each model in the stack. Multiply by three models and two hardware generations across the fleet. The deployment pipeline, not the models, was the bottleneck."
Solution
Kronos compiled the team's full perception, prediction, and planning stack for Drive AGX Orin and Jetson from the same source. Custom fusion modules compiled natively. The per-target conversion pipeline collapsed into one guided run per hardware variant. Subsequent targets and JetPack upgrades hit cached kernels and recompile in minutes.
Real numbers, on real silicon.
3
Full model stacks compiled per vehicle
days
Not months, per new fleet target
Up to 4x
Speedup on full models
Your stack. Ship it to every vehicle.
See Kronos compile your full model stack for the NVIDIA targets in your fleet today, and the ones you're rolling out next.
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.
