Description
Sixfab AI HAT+ for Raspberry Pi 5: Edge AI Acceleration with the DEEPX NPU
Run vision AI workloads on Raspberry Pi 5 in real time. Locally, no cloud, no GPU. Plug in the HAT+, install one APT package, and ship inference on your own hardware.
Choose your TOPS
Same PCB, same HAT+ form factor, same software stack. The NPU module is the only difference. Pick the variant that fits your workload and budget.
AI HAT+ 13 TOPS
$63AI HAT+ 25 TOPS
$90Raspberry Pi 5 native. DEEPX-class inference.
As an Official Raspberry Pi Design Partner, Sixfab integrates the DEEPX DX-M1M family directly onto a HAT+ compliant board, giving Pi 5 developers production-grade NPU acceleration over native PCIe without leaving the Raspberry Pi ecosystem.
DEEPX DX-M1M, 2 GB LPDDR4X · 13 TOPS variant with DX-M1ML
Native Pi 5 PCIe via 16-pin FFC cable. No USB hops, no bandwidth bottleneck
3 W typical NPU draw · ~13–15 W combined Pi 5 + HAT+ under load
Raspberry Pi HAT+ EEPROM auto-config · 56.5 × 65 mm · stacking-friendly
Bring AI to the edge
The edge AI ecosystem for Raspberry Pi 5. One NPU, one SDK, three form factors for real-world deployment. Never rebuild your stack.
This Product
On-device AI
13 / 25 TOPSAI acceleration for Raspberry Pi 5. Up to 25 TOPS of local AI performance. Plug in, install via APT, and start inferencing in minutes.
Connected edge systems
25 TOPS · LTE/5GAdd LTE/5G connectivity, NVMe local storage, and multi-camera support for real field deployments in a single under-board stack.
Industrial edge AI deployment
25 TOPS · −20 to +60 °CFanless, rugged, always-online edge AI computer for fleets and distributed industrial sites. Secure by design, IP40-rated, ALPON™ CLOUD-managed.
A live pipeline, one board, on-device AI
Frames flow in. The Pi 5 hosts your app. The DEEPX NPU does the neural math. Results come back. Watch the data move.
Capture
MIPI CSI, USB UVC, or RTSP IP cameras feed frames into the Raspberry Pi 5.
up to 4×1080pHost
Pi 5’s Cortex-A76 CPU runs your app: pre-processing, control flow, I/O, network.
Raspberry Pi 5Inference
DEEPX DX-M1M or DX-M1ML NPU runs your compiled DXNN model over PCIe Gen 3 ×1.
25 TOPS · INT8Use results
Detections, segments, and classifications return to your app. Display, log, trigger, stream.
30-35 FPSProduction-grade NPU. Raspberry Pi simplicity.
A HAT+ specification compliant accelerator that drops onto the Pi 5 you already know, with no third-party SDKs, no driver hacks, and no architectural commitments you can’t undo later.
Soldered NPU
Soldered DEEPX silicon transfers heat into the PCB far more efficiently than a socketed M.2 card. No socket to fail, no module slop, no third-party variability.
~3 W typical
NPU draws 2.5–3 W typically. Combined Pi 5 + HAT+ runs at 13–15 W on the official 27 W PSU.
APT install
Signed Sixfab repository ships dxrt-runtime, kernel driver, and tools. Update with apt update.
DXNN SDK
Bring ONNX models from PyTorch, TensorFlow, or Keras. Compile with DX-COM. Deploy with the C++ or Python runtime.
Sixfab AI HAT+: at a glance
The essentials. Every value here is sourced from R&D. For the full electrical, mechanical, and software reference, see the Hardware Reference docs.
dxrt-runtime · APT install · Python & C++ APIsRun a pre-built model, or bring your own
Sixfab gives you two complementary ways to get vision AI running on Raspberry Pi 5. Pick the path that matches your time-to-demo goal.
Sixfab Model Zoo
Pre-compiled DXNN models · ready to run · no training required.
A curated set of pre-optimized models for common vision tasks: YOLOv8n, MobileNet, ResNet, and more, already compiled for the DEEPX NPU. Download, deploy, run. Use it for evaluation, classroom demos, or as a starting point for your own pipeline.
Browse the Sixfab Model ZooDEEPX DXNN SDK
Full custom model deployment · ONNX in, DXNN out · Python & C++ APIs.
Take a model you’ve trained yourself in PyTorch, TensorFlow, or Keras. Export to ONNX, compile to DXNN with DX-COM, and run it on the NPU through the Python or C++ runtime. INT8 quantization is automatic, with ~2 % accuracy delta vs the FP32 source.
Open the DXNN SDK guideReal-world use cases
AI HAT+ runs vision AI workloads locally on a Raspberry Pi 5, which makes it a fit anywhere “no cloud” or “low latency” is the requirement and a discrete GPU is overkill.
Video analytics cameras
On-device object detection, counting, intrusion analytics, and retail insights on a single Pi 5 unit. Process frames locally, transmit only events upstream.
Robotics & autonomous systems
Real-time perception, object tracking, and navigation assistance on AMRs, robot arms, and visual-inspection rigs. Zero cloud-round-trip latency.
Smart city & infrastructure
Traffic monitoring, facility management, and safety systems on roadside Pi 5 units. Aggregate metadata over LTE, keep raw video on-device.
Industrial automation
Defect detection, quality inspection, and process monitoring on the production floor. Run offline. Survive network outages without losing inference.
Drones & autonomous systems
On-board perception with low weight and ~3 W typical NPU draw. Full inference capability during flight without a discrete GPU power budget.
Edge servers & AIoT
Compact inference nodes for multi-camera deployments. Distributed edge intelligence with the Raspberry Pi 5 ecosystem behind the SoC.
Compliance & certifications
Start running edge AI today, from $63
Sixfab AI HAT+ brings DEEPX-class NPU inference to Raspberry Pi 5. Open documentation. Open benchmarks. One APT install away.