{"product_id":"edge-ai-expansion-board-raspberry-pi-5","title":"Edge AI Expansion Board for Raspberry Pi 5","description":"\u003cdiv class=\"sxd\"\u003e\n\u003cdiv class=\"sxd-intro\"\u003e\n\u003cp class=\"sxd-lead\"\u003eBuild connected edge AI systems on Raspberry Pi 5. Sixfab Edge AI Expansion Board combines DEEPX NPU acceleration, NVMe SSD storage, and LTE\/5G connectivity in a single under-board platform designed for connected field deployments.\u003c\/p\u003e\n\u003cp\u003e25 TOPS · INT8 (DX-M1 M.2 NPU) · PCIe Gen 2\/3 x1 · triple M.2 (AI + NVMe + LTE) · USB-C PD, 27 W min, 45 W recommended. For Raspberry Pi 5 only.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-stats\"\u003e\n\u003ch3 class=\"sxd-vh\"\u003ePerformance at a glance\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e25 TOPS\u003c\/strong\u003e\u003cspan\u003eINT8 · DEEPX DX-M1 M.2\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTriple M.2\u003c\/strong\u003e\u003cspan\u003eAI + NVMe + LTE\/5G\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUSB-C PD\u003c\/strong\u003e\u003cspan\u003e27 W min · 45 W recommended\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eHighlights\u003c\/h3\u003e\n\u003cul class=\"sxd-dl\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eSwappable M.2 AI module\u003c\/strong\u003e\u003cspan\u003eDEEPX DX-M1 (25 TOPS at INT8) on a removable M.2 module. Bundled in the “Board + DEEPX DX-M1” variant · not included in “Board only”.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePCIe Gen 2 \/ Gen 3 x1\u003c\/strong\u003e\u003cspan\u003eDedicated PCIe lane for NPU ensuring stable high-throughput inference.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTriple M.2 architecture\u003c\/strong\u003e\u003cspan\u003eSeparate lanes for AI (PCIe), NVMe storage, and LTE\/5G connectivity running in parallel.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUSB-C PD\u003c\/strong\u003e\u003cspan\u003eSingle-cable power input that also back-powers Raspberry Pi 5 via pogo pins.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDXNN SDK + Model Zoo\u003c\/strong\u003e\u003cspan\u003ePre-compiled models or full ONNX → DXNN deployment pipeline.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOn-device inference, no cloud\u003c\/strong\u003e\u003cspan\u003eFully offline AI processing with no cloud dependency.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eTechnical specifications\u003c\/h3\u003e\n\u003ctable class=\"sxd-spec\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eAI accelerator\u003c\/th\u003e\n\u003ctd\u003eDEEPX DX-M1 · 25 TOPS at INT8 · on swappable M.2 module\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eNPU memory\u003c\/th\u003e\n\u003ctd\u003e4 GB LPDDR5 @ 5600 MT\/s on DX-M1 module\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eHost interface\u003c\/th\u003e\n\u003ctd\u003ePCIe Gen 2\/3 ×1 over 40 mm FFC cable\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eM.2 slots\u003c\/th\u003e\n\u003ctd\u003e3× M.2 · AI M-Key · NVMe M-Key · LTE\/5G Key-B\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eNVMe storage\u003c\/th\u003e\n\u003ctd\u003e2230\/2242\/2260\/2280 · 380-440 MB\/s read · 350-410 MB\/s write\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eCellular\u003c\/th\u003e\n\u003ctd\u003eM.2 Key-B · 1× nano SIM · eUICC supported\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePower input\u003c\/th\u003e\n\u003ctd\u003eUSB-C PD · 27 W min · 45 W recommended\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePi back-power\u003c\/th\u003e\n\u003ctd\u003e2× 5V + 2× GND pogo pins · 3 A per pin\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eForm factor\u003c\/th\u003e\n\u003ctd\u003e88.46 × 89.19 mm · ~36.26 mm Z-stack with Pi 5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eOperating temp\u003c\/th\u003e\n\u003ctd\u003e0-70 °C commercial\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eSupported host\u003c\/th\u003e\n\u003ctd\u003eRaspberry Pi 5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eHost OS\u003c\/th\u003e\n\u003ctd\u003eRaspberry Pi OS (Trixie)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eRuntime\u003c\/th\u003e\n\u003ctd\u003edxrt-runtime · APT install · Python \u0026amp; C++ APIs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eModel pipeline\u003c\/th\u003e\n\u003ctd\u003eONNX → DXNN via DX-COM compiler\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eHot-plug\u003c\/th\u003e\n\u003ctd\u003eNot supported. Power off Pi 5 before mounting\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr class=\"sxd-group\"\u003e\u003cth colspan=\"2\"\u003eCompliance\u003c\/th\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eCertification\u003c\/th\u003e\n\u003ctd\u003eCertification in progress: CE, FCC, UKCA, RoHS, REACH\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eUse cases\u003c\/h3\u003e\n\u003cul class=\"sxd-dl\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eConnected smart cameras\u003c\/strong\u003e\u003cspan\u003eRun real-time object detection or face recognition on-device. Transmit metadata and alerts over LTE\/5G. Raw video stays local.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRobotics \u0026amp; autonomous drones\u003c\/strong\u003e\u003cspan\u003eEquip mobile platforms with real-time vision AI and always-on cellular uplink. Make on-device decisions at full frame rate.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOutdoor vision systems\u003c\/strong\u003e\u003cspan\u003eCount and classify vehicles, pedestrians, or wildlife from remote edge units. Aggregate metadata over cellular while keeping raw video fully on-device for privacy.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRemote monitoring \u0026amp; sensors\u003c\/strong\u003e\u003cspan\u003eMonitor equipment or infrastructure in unattended environments. Run anomaly detection locally with the NPU and transmit only event-based alerts over LTE.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIoT sensor fusion\u003c\/strong\u003e\u003cspan\u003eCombine camera streams, sensor data, and telemetry for multi-modal edge inference. Store processed results locally on NVMe, sync summaries to the cloud on your schedule.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLive field testing \u0026amp; staging\u003c\/strong\u003e\u003cspan\u003eValidate AI models in real-world environments before ALPON X5 AI deployment. The Expansion Board runs the same DEEPX SDK and models, zero porting effort when you scale up.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eWhat's in the box\u003c\/h3\u003e\n\u003cul class=\"sxd-box\"\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eAI Expansion Board\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×1\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eUSB 3.0 Bridge\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×1\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003ePCIe Cable\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×1\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eM2.5 5mm M-F Plastic Spacer\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×4\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eM2.5 15mm F-F Plastic Spacer\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×6\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eM2.5 Screw\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×6\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eUSB Type-C Plastic Cap\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×1\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eM2 Flathead Screw\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×3\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eM2 Module Plastic Spacer\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×3\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"sxd-fine\"\u003eBoard only – board + mounting kit (9 items above). Board + DEEPX DX-M1 – board + mounting kit + AI module: adds the DEEPX DX-M1 M.2 AI Module (25 TOPS · INT8) ×1.\u003c\/p\u003e\n\u003ch4 class=\"sxd-sub\"\u003eNot included (sold separately)\u003c\/h4\u003e\n\u003ctable class=\"sxd-kv sxd-ni\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eRequired\u003c\/th\u003e\n\u003ctd\u003eRaspberry Pi 5 (host board) · USB-C PD power supply (27 W min, 45 W recommended) · DEEPX DX-M1 M.2 AI module (for “Board only”; bundled in “Board + DEEPX DX-M1”)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eOptional\u003c\/th\u003e\n\u003ctd\u003eNVMe SSD (M.2 2230, 2242, 2260, or 2280) · LTE\/5G M.2 modem module (M.2 Key-B) · nano SIM card (only if using cellular) · cellular antennas (connector type depends on the chosen modem module)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eCompatibility\u003c\/h3\u003e\n\u003cdiv class=\"sxd-host\"\u003e\n\u003cdiv class=\"sxd-host-yes\"\u003e\n\u003cp class=\"sxd-k\"\u003eSupported host\u003c\/p\u003e\n\u003cul\u003e\u003cli\u003eRaspberry Pi 5\u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-host-no\"\u003e\n\u003cp class=\"sxd-k\"\u003eNot supported\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eRaspberry Pi 4\u003c\/li\u003e\n\u003cli\u003eCompute Module 4\u003c\/li\u003e\n\u003cli\u003eNon-Raspberry Pi SBCs\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eFrequently asked questions\u003c\/h3\u003e\n\u003cdiv class=\"sxd-faq\"\u003e\n\u003cdetails open\u003e\u003csummary\u003eHow is it different from the Sixfab AI HAT+?\u003c\/summary\u003e\u003cp\u003eThe AI HAT+ targets early-stage AI prototyping. The Expansion Board adds dedicated NVMe and LTE\/5G M.2 slots alongside DEEPX AI acceleration, with all components running in parallel on the same board through a single USB-C connection.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes it require cloud connectivity for AI inference?\u003c\/summary\u003e\u003cp\u003eNo. AI inference runs fully on-device using the DEEPX NPU. The cellular connection transmits metadata, alerts, or application-level outputs.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat AI models can I run?\u003c\/summary\u003e\u003cp\u003eThe platform supports models compiled for the DEEPX DX-M1 NPU family. This includes YOLO-family models, pose estimation networks, and CNN-based architectures such as VGG-type models.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I run multi-camera vision workloads?\u003c\/summary\u003e\u003cp\u003eYes. The Raspberry Pi 5 platform supports multi-camera configurations, enabling multi-angle monitoring, inspection workflows, and spatial vision applications.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow does NVMe storage help in edge AI?\u003c\/summary\u003e\u003cp\u003eNVMe storage enables high-speed local data logging and buffering for AI pipelines: store datasets locally, buffer inference results, reduce reliance on cloud storage, and improve performance for continuous workloads.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow does the Expansion Board fit into the Sixfab product line?\u003c\/summary\u003e\u003cp\u003eIt is Stage 2 in a unified DEEPX NPU ecosystem: Stage 1: AI HAT+ for rapid prototyping; Stage 2: Edge AI Expansion Board for connected field deployments; Stage 3: ALPON X5 AI for rugged industrial systems.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat is the difference between the Expansion Board and the ALPON X5 AI?\u003c\/summary\u003e\u003cp\u003eThe Edge AI Expansion Board is designed for Raspberry Pi 5-based deployments, making it ideal for development, testing, and connected field systems. ALPON X5 AI is a fully integrated industrial edge AI computer with a rugged enclosure, extended temperature range, hardware reliability features, and fleet management capabilities.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhy choose the Expansion Board over a standalone AI HAT for field deployment?\u003c\/summary\u003e\u003cp\u003eA standalone AI HAT handles inference. A field deployment also needs local data logging and a connectivity layer that doesn't depend on Wi-Fi. The Expansion Board solves all on the same board: DEEPX NPU for inference, NVMe SSD for high-speed local storage, LTE\/5G M.2 slot for cellular uplink.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCloud-connected vs. Expansion Board approach?\u003c\/summary\u003e\u003cp\u003eWith a cloud-connected setup, raw video or sensor data leaves the device for remote processing - adding latency, bandwidth cost, and a dependency on network availability. The Expansion Board runs DEEPX NPU inference fully on-device. LTE\/5G transmits only the output: metadata, alerts, or event triggers.\u003c\/p\u003e\u003c\/details\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Sixfab","offers":[{"title":"Board only","offer_id":48916963393763,"sku":"S164","price":60.0,"currency_code":"EUR","in_stock":true},{"title":"Board + DEEPX DX-M1","offer_id":48953914884323,"sku":"S173","price":190.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0795\/4618\/8003\/files\/Edge-AI-Expansion-Board-23.jpg?v=1781616900","url":"https:\/\/eu.sixfab.com\/products\/edge-ai-expansion-board-raspberry-pi-5","provider":"Sixfab","version":"1.0","type":"link"}