Run perception and language models where the action is — on the robot, not in the cloud. Low latency, private, always available. We build and optimize the models, and supply the edge compute they run on.
Edge AI inference runs models directly on the device — a robot, camera or controller — instead of sending data to the cloud. For physical systems that means real-time perception and control within a single-digit-millisecond budget, operation without connectivity, and data that never leaves the machine. Haink delivers both halves: the optimized model and the edge hardware it runs on.
Object detection, segmentation, pose estimation and scene understanding running locally on the robot — tuned to your environment and camera setup.
Compact language, vision-language-action (VLA) and reasoning vision-language models (such as NVIDIA Cosmos Reason) for on-board reasoning, instruction following and human interaction without a cloud round-trip.
Fusing multiple cameras, depth, LiDAR and radar on-device with synchronized, low-latency perception — the input layer modern VLA models depend on.
Quantization, pruning and runtime tuning (TensorRT and similar) so models fit the latency, memory and power budget of the target edge platform.
Packaging, on-device runtime and over-the-air model updates with monitoring — so inference stays reliable after it ships.
Detect defects on the production line in real time, on-device, with no cloud round-trip and no line data leaving the floor.
Vision-guided manipulation of varied or unfamiliar parts, where a fixed program would fail and a learned policy adapts.
On-board navigation, obstacle avoidance and perception for AMRs and AGVs that must keep running when connectivity drops.
Vision-language-action models that turn a spoken or written instruction into action, on-device and within the control loop.
Chosen by compute, power and safety needs — configured and supplied alongside sensors and controllers.
| Platform | Typical use |
|---|---|
| NVIDIA Jetson Orin Nano | Entry prototypes, lightweight perception, low power (~67 TOPS, 8 GB) |
| NVIDIA Jetson AGX Orin | Production robots, multi-camera perception + control (~275 TOPS, up to 64 GB) |
| NVIDIA Jetson AGX Thor | 2026 flagship — humanoids and on-device VLA / reasoning VLMs (~2,070 FP4 TFLOPS, 128 GB) |
| NVIDIA IGX Thor / IGX Orin | Safety- and reliability-critical industrial / medical edge (ISO 26262, IEC 61508) |
Not sure which to pick? See Jetson Orin vs Thor → · Browse robotics & physical-AI hardware →
We define the task, environment and the hard constraints — latency, power budget and safety — and pick the target edge platform.
Perception-to-action working on the real edge hardware, so the budget is proven on the target, not on a workstation.
Quantization, pruning and runtime tuning (TensorRT) until the model meets the latency, memory and power budget.
Packaging, on-device runtime, over-the-air updates and monitoring so inference stays reliable after it ships.
Illustrative target profile for a single-camera visual-inspection cell on Jetson AGX Orin — representative figures, not a specific client result.
| Metric | Target |
|---|---|
| Inference latency | ~8 ms per frame |
| Throughput | 30+ FPS per camera |
| Defect recall (target classes) | ≥ 99% |
| Cloud dependency | None — fully on-device, offline-capable |
| Power envelope | < 40 W |
We agree these targets up front and prove them on the target hardware during the prototype stage.
Edge AI inference runs machine-learning models directly on a device — a robot, camera or controller — rather than in the cloud. It delivers low latency, works without connectivity, and keeps data local, which is essential for real-time perception and control in physical systems.
In 2026, NVIDIA Jetson AGX Thor and IGX Thor for advanced and safety-critical robots, with Orin Nano, AGX Orin and IGX Orin for lighter workloads — chosen by compute, power and safety requirements. Haink configures and supplies these alongside cameras, sensors and controllers.
Choose Orin for prototypes, vision AI and most production robots where its compute is enough; choose Thor when running a large vision-language-action model, a reasoning VLM, or heavy multi-sensor fusion that needs its FP4/FP8 compute and 128 GB. See our Jetson Orin vs Thor guide.
Yes. We deploy compact VLA and reasoning vision-language models on-device, typically on Jetson AGX Thor, with optimization (quantization, pruning, TensorRT) to meet the robot's latency and power budget.
Yes — our in-house AI team adapts and optimizes models for on-device inference (quantization, pruning, runtime tuning) so they meet the latency and power budget of the target edge platform.
Tell us the task and the constraints — we’ll propose a model approach and an edge-hardware design.