We take research models out of PyTorch and put them into production — quantized, compiled, and profiled on the silicon that actually ships. Not cloud. Not server GPU. Edge.
ONNX export, graph surgery, and INT8/FP16 quantization — tuned for the target silicon's compiler, not the framework default.
Production inference code, not notebooks. Memory-safe, deterministic, and integrated into ROS2 or your existing stack.
Nsight, hardware-native profilers, and real latency budgets — every millisecond accounted for before it ships.
214 TOPS INT8 · Native SDK deployment
Up to 275 TOPS · CUDA / TensorRT
Next-gen Jetson class · TensorRT
Hexagon DSP · SNPE toolchain
OpenVINO · NPU + integrated GPU
26 TOPS · HailoRT
Onboard perception for mobile robots and manipulators — no round-trip to the cloud.
Mission hardware, no compromises. Deployed onboard an interceptor drone.
Automotive latency budgets for detection, tracking, and depth on embedded silicon.
Multi-stream detection and tracking that runs at the edge, where the cameras are.
Inspection models compiled for the edge boxes already on the factory floor.
Monocular 3D object detection compiled and deployed end-to-end on the Metis M.2 AIPU.
Full monocular depth pipeline optimized for single-core throughput — 30 FPS sustained end-to-end.
Single-object tracker with a split architecture — proven onboard an interceptor drone platform.
ZipDepth was featured on Axelera AI's official engineering blog.
Tell me about your model, your target hardware, and your deadline. I'll reply within a day.
sanket@zeropoint-vision.com