{"title":"Best Sellers","description":"\u003cp\u003eThe AI boards, cellular kits, and enclosures our customers order most.\u003c\/p\u003e","products":[{"product_id":"raspberry-pi-4g-lte-modem-kit","title":"Sixfab 4G\/LTE Cellular Modem Kit for Raspberry Pi","description":"\u003cdiv class=\"sxd\"\u003e\n\u003cdiv class=\"sxd-intro\"\u003e\n\u003cp class=\"sxd-lead\"\u003eThis product includes default components for easy connection to 4G networks. With the Raspberry Pi 4G\/LTE Modem Kit, your Raspberry Pi based projects will access data networks all around the world.\u003c\/p\u003e\n\u003cp\u003eDesigned exclusively for Raspberry Pi, the kit offers seamless 4G LTE network integration on the pre-certified Sixfab Base HAT. With antennas, cables, headers, and spacers, it's ready to connect right out of the box, and the included $25 data credit coupon code requires no commitment.\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\u003e150 Mbps\u003c\/strong\u003e\u003cspan\u003eMax. DL · 50 Mbps UL\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e−25 → +70 °C\u003c\/strong\u003e\u003cspan\u003eOperating temperature\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e2 module options\u003c\/strong\u003e\u003cspan\u003eQuectel EG25-G · Telit LE910C4-EU\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\u003eSimple setup\u003c\/strong\u003e\u003cspan\u003eStep-by-step tutorials make connecting easy for everyone.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHigh speed\u003c\/strong\u003e\u003cspan\u003eHigh-speed. Low latency bring powerful IoT performance.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDesigned for Raspberry Pi\u003c\/strong\u003e\u003cspan\u003eSeamless network integration \u0026amp; reliable connection - designed for Raspberry Pi 4 \u0026amp; 5.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePre-certified carrier board\u003c\/strong\u003e\u003cspan\u003eUtilize the pre-certified Sixfab Base HAT for reliable carrier board support.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGlobal or regional data\u003c\/strong\u003e\u003cspan\u003eChoose global or regional data plans to suit your project's needs; the kit includes a $25 data credit coupon code, no commitment required.\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\"\u003eBest for\u003c\/th\u003e\n\u003ctd\u003eEdge computing projects requiring high speeds\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eRegions\u003c\/th\u003e\n\u003ctd\u003eGlobal or Regional\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eMax. data speeds\u003c\/th\u003e\n\u003ctd\u003e150 Mbps (DL) \/ 50 Mbps (UL)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePositioning\u003c\/th\u003e\n\u003ctd\u003eGPS, GNSS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eElectrical\u003c\/th\u003e\n\u003ctd\u003e5V DC, 3.0 A\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eOperating temp\u003c\/th\u003e\n\u003ctd\u003e-25 °C to 70 °C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eApprovals\u003c\/th\u003e\n\u003ctd\u003eUS: FCC Part 15 Class B · CA: ICES-003 Class B · EU: CE\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eCompliance\u003c\/th\u003e\n\u003ctd\u003eRoHS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eLTE module options\u003c\/th\u003e\n\u003ctd\u003eQuectel EG25-G (North America \u0026amp; Global) · Telit LE910C4-EU (EMEA)\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\u003eWhat's in the box\u003c\/h3\u003e\n\u003cul class=\"sxd-box\"\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eSixfab 3G-4G\/LTE Base HAT for Raspberry Pi: Sixfab HAT Board, extra tall 40-pin GPIO stacking header, short 40-pin GPIO stacking header, plastic spacer kit\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\"\u003eLTE full band PCB antenna u.FL plug - 100mm\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\"\u003eLTE - GNSS Dual u.FL Antenna - 100mm\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\"\u003e4G\/LTE mini PCIe modem (options available)\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\"\u003eSixfab SIM card with $25 data credit\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×1\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\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\n\u003cli\u003eRaspberry Pi 4\u003c\/li\u003e\n\u003cli\u003eRaspberry Pi 5\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp class=\"sxd-note\"\u003eSixfab CORE will be discontinued on December 31, 2025. After this date, users of the Raspberry Pi 4G\/LTE Cellular Modem Kit will need to follow the ECM (Ethernet Control Model) tutorial on our website for setting up cellular connectivity without Sixfab CORE.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Sixfab","offers":[{"title":"Telit LE910C4-EU (EMEA)","offer_id":48916963524835,"sku":"B64","price":140.0,"currency_code":"EUR","in_stock":true},{"title":"EG25-G (North America \u0026 Global)","offer_id":48916963459299,"sku":"B54","price":140.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0795\/4618\/8003\/files\/Modem-kit-Main-eg25-shop.jpg?v=1778161868"},{"product_id":"ai-hat-plus-raspberry-pi-5","title":"Sixfab AI HAT+ for Raspberry Pi 5","description":"\u003cdiv class=\"sxd\"\u003e\n\u003cdiv class=\"sxd-intro\"\u003e\n\u003cp class=\"sxd-lead\"\u003eBuild and test vision AI models directly on Raspberry Pi 5. Sixfab AI HAT+ delivers compact DEEPX AI acceleration for rapid prototyping and local inference.\u003c\/p\u003e\n\u003cp\u003eRun 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. 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.\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\u003eUp to 25 TOPS\u003c\/strong\u003e\u003cspan\u003eINT8 · DEEPX DX-M1M\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePCIe Gen 3 ×1\u003c\/strong\u003e\u003cspan\u003eNative Pi 5 PCIe\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e~3 W\u003c\/strong\u003e\u003cspan\u003eTypical NPU draw\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\u003e25 TOPS at INT8\u003c\/strong\u003e\u003cspan\u003eDEEPX DX-M1M, 2 GB LPDDR4X · 13 TOPS variant with DX-M1ML.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePCIe Gen 3 ×1\u003c\/strong\u003e\u003cspan\u003eNative Pi 5 PCIe via 16-pin FFC cable. No USB hops, no bandwidth bottleneck.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e~3 W typical\u003c\/strong\u003e\u003cspan\u003e3 W typical NPU draw · ~13-15 W combined Pi 5 + HAT+ under load on the official 27 W PSU.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHAT+ spec compliant\u003c\/strong\u003e\u003cspan\u003eRaspberry Pi HAT+ EEPROM auto-config · 56.5 × 65 mm · stacking-friendly.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSoldered NPU\u003c\/strong\u003e\u003cspan\u003eSoldered 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.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAPT install\u003c\/strong\u003e\u003cspan\u003eSigned Sixfab repository ships dxrt-runtime, kernel driver, and tools. Update with apt update.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDXNN SDK\u003c\/strong\u003e\u003cspan\u003eBring ONNX models from PyTorch, TensorFlow, or Keras. Compile with DX-COM. Deploy with the C++ or Python runtime.\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-M1M (25 TOPS) or DX-M1ML (13 TOPS) at INT8\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eNPU memory\u003c\/th\u003e\n\u003ctd\u003e2 GB LPDDR4X (DX-M1M) · 1 GB LPDDR4X (DX-M1ML)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eHost interface\u003c\/th\u003e\n\u003ctd\u003ePCIe Gen 3 ×1 over 16-pin FFC cable\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eForm factor\u003c\/th\u003e\n\u003ctd\u003eRaspberry Pi HAT+ · 56.5 × 65 mm · 6.56 mm tall\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePower input\u003c\/th\u003e\n\u003ctd\u003e5 V via the Pi 5 2×20-pin header and PCIe connector\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eNPU power draw\u003c\/th\u003e\n\u003ctd\u003e3 W (typical)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eCooling\u003c\/th\u003e\n\u003ctd\u003ePassive\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\u003eTwo SKUs · one board\u003c\/h3\u003e\n\u003cp\u003eSame PCB, same HAT+ form factor, same software stack. The NPU module is the only difference. Pick the variant that fits your workload and budget.\u003c\/p\u003e\n\u003cdiv class=\"sxd-scroll\"\u003e\u003ctable class=\"sxd-table\"\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003ctd\u003e\u003c\/td\u003e\n\u003cth scope=\"col\"\u003eAI HAT+ 13 TOPS\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eAI HAT+ 25 TOPS\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eNPU\u003c\/th\u003e\n\u003ctd\u003eDEEPX DX-M1ML\u003c\/td\u003e\n\u003ctd\u003eDEEPX DX-M1M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePrecision\u003c\/th\u003e\n\u003ctd\u003eINT8\u003c\/td\u003e\n\u003ctd\u003eINT8\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eNPU memory\u003c\/th\u003e\n\u003ctd\u003e1 GB LPDDR4X\u003c\/td\u003e\n\u003ctd\u003e2 GB LPDDR4X\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eBest for\u003c\/th\u003e\n\u003ctd\u003eSingle-camera, single-model\u003c\/td\u003e\n\u003ctd\u003eMulti-model, multi-camera\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eTypical scenario\u003c\/th\u003e\n\u003ctd\u003eLow-power projects, cost-sensitive builds\u003c\/td\u003e\n\u003ctd\u003eHigh-resolution streams, headroom for growth\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\u003c\/div\u003e\n\u003cp class=\"sxd-note\"\u003eAvailable by Q4, 2026.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eHow it works\u003c\/h3\u003e\n\u003col class=\"sxd-steps\"\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-step-n\"\u003e01\u003c\/span\u003e\u003cstrong\u003eCapture\u003c\/strong\u003e\u003cspan\u003eMIPI CSI, USB UVC, or RTSP IP cameras feed frames into the Raspberry Pi 5 - up to 4×1080p.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-step-n\"\u003e02\u003c\/span\u003e\u003cstrong\u003eHost\u003c\/strong\u003e\u003cspan\u003ePi 5's Cortex-A76 CPU runs your app: pre-processing, control flow, I\/O, network.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-step-n\"\u003e03\u003c\/span\u003e\u003cstrong\u003eInference\u003c\/strong\u003e\u003cspan\u003eDEEPX DX-M1M or DX-M1ML NPU runs your compiled DXNN model over PCIe Gen 3 ×1 - 25 TOPS · INT8.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-step-n\"\u003e04\u003c\/span\u003e\u003cstrong\u003eUse results\u003c\/strong\u003e\u003cspan\u003eDetections, segments, and classifications return to your app. Display, log, trigger, stream - 30-35 FPS.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eTwo paths to deployment\u003c\/h3\u003e\n\u003cdiv class=\"sxd-cards\"\u003e\n\u003cdiv class=\"sxd-card\"\u003e\n\u003cp class=\"sxd-card-k\"\u003eOption 1\u003c\/p\u003e\n\u003ch4\u003eSixfab Model Zoo\u003c\/h4\u003e\n\u003cp\u003ePre-compiled DXNN models - ready to run, no training required. YOLOv8n, MobileNet, ResNet, and more, already compiled for the DEEPX NPU.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-card\"\u003e\n\u003cp class=\"sxd-card-k\"\u003eOption 2\u003c\/p\u003e\n\u003ch4\u003eDEEPX DXNN SDK\u003c\/h4\u003e\n\u003cp\u003eFull custom model deployment: export your PyTorch, TensorFlow, or Keras model to ONNX, compile to DXNN with DX-COM, and run it through the Python or C++ runtime. INT8 quantization is automatic, with ~2% accuracy delta vs the FP32 source.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eWhat you can build\u003c\/h3\u003e\n\u003cul class=\"sxd-dl\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eVideo analytics cameras\u003c\/strong\u003e\u003cspan\u003eOn-device object detection, counting, intrusion analytics, and retail insights on a single Pi 5 unit.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRobotics \u0026amp; autonomous systems\u003c\/strong\u003e\u003cspan\u003eReal-time perception, object tracking, and navigation assistance on AMRs, robot arms, and visual-inspection rigs.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSmart city \u0026amp; infrastructure\u003c\/strong\u003e\u003cspan\u003eTraffic monitoring, facility management, and safety systems on roadside Pi 5 units.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIndustrial automation\u003c\/strong\u003e\u003cspan\u003eDefect detection, quality inspection, and process monitoring on the production floor.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDrones \u0026amp; autonomous systems\u003c\/strong\u003e\u003cspan\u003eOn-board perception with low weight and ~3 W typical NPU draw.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEdge servers \u0026amp; AIoT\u003c\/strong\u003e\u003cspan\u003eCompact inference nodes for multi-camera deployments.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eSixfab edge AI ecosystem\u003c\/h3\u003e\n\u003cp\u003eOne NPU, one SDK, three form factors for real-world deployment.\u003c\/p\u003e\n\u003cul class=\"sxd-rows\"\u003e\n\u003cli\u003e\n\u003cdiv class=\"sxd-row-h\"\u003e\n\u003cstrong\u003eAI HAT+\u003c\/strong\u003e\u003cspan\u003eThis product\u003c\/span\u003e\n\u003c\/div\u003e\n\u003cp\u003e13 \/ 25 TOPS AI acceleration for Raspberry Pi 5.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv class=\"sxd-row-h\"\u003e\n\u003ca href=\"\/products\/edge-ai-expansion-board-raspberry-pi-5\"\u003eSixfab Edge AI Expansion Board\u003c\/a\u003e\u003cspan\u003e25 TOPS · LTE\/5G\u003c\/span\u003e\n\u003c\/div\u003e\n\u003cp\u003eAdd LTE\/5G connectivity, NVMe local storage, and multi-camera support in a single under-board stack.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv class=\"sxd-row-h\"\u003e\n\u003ca href=\"\/products\/alpon-x5-ai\"\u003eALPON X5 AI\u003c\/a\u003e\u003cspan\u003e25 TOPS · −20 to +60 °C\u003c\/span\u003e\n\u003c\/div\u003e\n\u003cp\u003eFanless, rugged, always-online edge AI computer for fleets and distributed industrial sites.\u003c\/p\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\"\u003eSixfab AI HAT+ board\u003cspan class=\"sxd-box-s\"\u003e13 TOPS · INT8 (DEEPX DX-M1ML) or 25 TOPS · INT8 (DEEPX DX-M1M), depending on the option\u003c\/span\u003e\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 FFC cable (16-pin)\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\"\u003e16 mm stacking header (2×20, 2.54 mm)\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 × 16 mm F-F 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 × 5 mm plastic screw\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×8\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003ePassive cooler (with thermal pad)\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×1\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"sxd-fine\"\u003eBoth options include the exact same 6-item mounting kit and assembly hardware; the only difference is which DEEPX NPU is soldered on the board. For Raspberry Pi 5 only.\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) · official 27 W USB-C PD power supply for the Raspberry Pi 5 · microSD card flashed with Raspberry Pi OS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eRecommended\u003c\/th\u003e\n\u003ctd\u003eRaspberry Pi Active Cooler for the Pi 5 (for sustained 100% NPU utilization)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eOptional\u003c\/th\u003e\n\u003ctd\u003eUSB or CSI camera (for live vision inference workloads)\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\u003eCompute Module 5 on the CM5 IO Board\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\u003ctable class=\"sxd-kv sxd-compat\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eCameras\u003c\/th\u003e\n\u003ctd\u003e\n\u003cspan class=\"sxd-item\"\u003eRaspberry Pi Camera Modules \u003cspan class=\"sxd-muted\"\u003e(MIPI CSI)\u003c\/span\u003e\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003eUSB cameras \u003cspan class=\"sxd-muted\"\u003e(UVC)\u003c\/span\u003e\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003eIP cameras \u003cspan class=\"sxd-muted\"\u003e(RTSP)\u003c\/span\u003e\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003eMulti-camera configurations\u003c\/span\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eFrameworks\u003c\/th\u003e\n\u003ctd\u003e\n\u003cspan class=\"sxd-item\"\u003eONNX \u003cspan class=\"sxd-muted\"\u003e(primary)\u003c\/span\u003e\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003ePyTorch\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003eTensorFlow\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003eKeras\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003eUltralytics YOLO \u003cspan class=\"sxd-muted\"\u003e(native integration coming soon)\u003c\/span\u003e\u003c\/span\u003e\n\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\u003eFrequently asked questions\u003c\/h3\u003e\n\u003cdiv class=\"sxd-faq\"\u003e\n\u003cdetails open\u003e\u003csummary\u003eWhat's the difference between 13 TOPS and 25 TOPS?\u003c\/summary\u003e\u003cp\u003e13 TOPS (DEEPX DX-M1ML): single-model, single-camera deployments. Low power. 25 TOPS (DEEPX DX-M1M): multi-model pipelines, multi-camera, high-resolution. Recommended for most projects. Both share the same PCB, HAT+ form factor, software stack, and DXNN SDK. Only the soldered NPU module differs.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow do I decide whether 13 or 25 TOPS is right for my project?\u003c\/summary\u003e\u003cp\u003eSize it from three things: how many camera streams and models you run at once, your target input resolution, and whether you want headroom for future models. Pick 13 TOPS (DX-M1ML) for single-camera, single-model pipelines, prototypes, and budget-sensitive builds at common resolutions. Pick 25 TOPS (DX-M1M) for multi-stream 1080p, higher-resolution single-stream, multi-model pipelines, or larger compiled models - it is the safe default when you want room to grow. To estimate before you buy, check the per-model FPS figures in the Sixfab Model Zoo against your target frame rate and stream count.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow does it compare to the Raspberry Pi AI HAT+ and AI HAT+ 2?\u003c\/summary\u003e\u003cp\u003eAll three are HAT+ form-factor accelerators that mount on a Raspberry Pi 5 and run inference on a soldered NPU over PCIe. The difference is the silicon: Sixfab AI HAT+ uses DEEPX (DX-M1ML at 13 TOPS or DX-M1M at 25 TOPS, INT8); the Raspberry Pi boards use Hailo. The Raspberry Pi AI HAT+ ships as Hailo-8L (13 TOPS) or Hailo-8 (26 TOPS); for vision, Sixfab's 25 TOPS at INT8 is competitive with the 26 TOPS Hailo-8 model, and YOLOv8n at 640×640 runs 30-35 FPS on a Raspberry Pi 5 with 8 GB RAM. The newer Raspberry Pi AI HAT+ 2 (Hailo-10H, 40 TOPS at INT4, 8 GB on-board RAM) adds on-device LLMs and VLMs; the Sixfab AI HAT+ is vision-focused today. If your project specifically needs local generative AI right now, the Raspberry Pi AI HAT+ 2 is built for that workload.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I run LLMs on this?\u003c\/summary\u003e\u003cp\u003eNo. DEEPX DX-M1M and DX-M1ML are optimized for computer vision (object detection, segmentation, classification). The current generation doesn't support LLMs. LLMs are on the DEEPX roadmap and Sixfab will support them as the silicon enables.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow long does setup take?\u003c\/summary\u003e\u003cp\u003eUnder 15 minutes: power off the Pi 5, mount the AI HAT+, connect the 16-pin FFC cable, install the dxrt-runtime APT package, verify with lspci | grep DEEPX, run a Model Zoo demo, and see YOLOv8n at 640×640 hit 30-35 FPS on Raspberry Pi 5 with 8 GB RAM. Custom DXNN SDK deployments take 1-2 hours for ONNX export, DXNN compilation, and application integration.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes it work offline?\u003c\/summary\u003e\u003cp\u003eYes, completely. Inference runs entirely on-device over the PCIe Gen 3 x1 link between the Raspberry Pi 5 and the DEEPX NPU. No cloud, no GPU, no external connectivity required.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhich Raspberry Pi models are supported?\u003c\/summary\u003e\u003cp\u003eRaspberry Pi 5 is the only supported host. Not supported: Raspberry Pi 4, Compute Module 4, the Raspberry Pi Compute Module 5 on the Raspberry Pi CM5 IO Board, and non-Raspberry Pi SBCs. Hot-plug is not supported - power off the Raspberry Pi 5 before mounting or removing the AI HAT+.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat camera formats are supported?\u003c\/summary\u003e\u003cp\u003eRaspberry Pi Camera Modules (MIPI CSI), USB cameras (UVC), IP cameras (RTSP), and multi-camera configurations. Cameras connect directly to the Raspberry Pi 5; the AI HAT+ does not obstruct the Pi 5's CSI connectors.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat AI frameworks are supported?\u003c\/summary\u003e\u003cp\u003eONNX (primary), PyTorch, TensorFlow, Keras, and Ultralytics YOLO (native integration coming soon). Models are exported to ONNX, then compiled to DXNN via the DEEPX DXNN SDK for execution on the NPU. The Sixfab Model Zoo includes pre-compiled models (YOLOv8n, YOLOv8s, MobileNet, ResNet, and others) ready to deploy.\u003c\/p\u003e\u003c\/details\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Sixfab","offers":[{"title":"25 TOPS (DX-M1M)","offer_id":48916963295459,"sku":"S166","price":90.0,"currency_code":"EUR","in_stock":true},{"title":"13 TOPS (DX-M1ML)","offer_id":48953913999587,"sku":"N\/A","price":63.0,"currency_code":"EUR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0795\/4618\/8003\/files\/Sixfab-AI-HAT-Plus-11.jpg?v=1781616885"},{"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"},{"product_id":"raspberry-pi-ip65-outdoor-iot-project-enclosure","title":"Sixfab IP65 Outdoor Project Enclosure for Raspberry Pi \u0026 Development Boards","description":"\u003cdiv class=\"sxd\"\u003e\n\u003cdiv class=\"sxd-intro\"\u003e\n\u003cp class=\"sxd-lead\"\u003eThis enclosure provides environmental protection for IoT projects, with the flexibility to add or remove cable interfaces to your project in seconds. Its IP65-rated lid and grommets protect against dust, rain, and snow.\u003c\/p\u003e\n\u003cp\u003eIt includes a clear polycarbonate lid, a built-in gasket seal, mounting ears, and four different grommet types for various cable types.\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\u003eIP-65\u003c\/strong\u003e\u003cspan\u003eDust, rain and snow protection\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e212 × 123 × 60 mm\u003c\/strong\u003e\u003cspan\u003eWidth × length × height\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eABS\u003c\/strong\u003e\u003cspan\u003eRF-friendly plastic\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\u003eSimplified ingress protection\u003c\/strong\u003e\u003cspan\u003eA reliable solution for ingress protection, crucial for various IoT project needs, eliminating the need for complex machining.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVersatile compatibility\u003c\/strong\u003e\u003cspan\u003eWorks with Raspberry Pi, Beaglebone, Tinker Board, and stackable HATs.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eWeatherproof design\u003c\/strong\u003e\u003cspan\u003eWater\/weatherproof plastic structure protecting from moisture, rain, snow, wind, and dust.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEasy mounting\u003c\/strong\u003e\u003cspan\u003eWall, pole, or custom mounting with the included components.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSpacious interior\u003c\/strong\u003e\u003cspan\u003eAccommodates batteries, sensors, Raspberry Pi Camera, and electronics, with sealed grommets for varied cable diameters (5 IP65 and 5 IP54 certified cable insert plugs included).\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRF-friendly material\u003c\/strong\u003e\u003cspan\u003eMade from RF-friendly plastic (ABS), allowing antenna placement within the enclosure or external antenna use through grommets.\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\"\u003eBest for\u003c\/th\u003e\n\u003ctd\u003eOutdoor Raspberry Pi projects requiring environmental protection\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eMaterial\u003c\/th\u003e\n\u003ctd\u003eABS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eFeatures\u003c\/th\u003e\n\u003ctd\u003ePolycarbonate clear cover with gasket seal · customizable IP-65 cable grommets · wall mount · inner base plate\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eIP rating\u003c\/th\u003e\n\u003ctd\u003eIP-65\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eApplication\u003c\/th\u003e\n\u003ctd\u003eOutdoor; heavy duty\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eWidth\u003c\/th\u003e\n\u003ctd\u003e212 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eLength\u003c\/th\u003e\n\u003ctd\u003e123 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eHeight\u003c\/th\u003e\n\u003ctd\u003e60 mm\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\u003eWhat's in the box\u003c\/h3\u003e\n\u003cul class=\"sxd-box\"\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eIP65 Enclosure Case Bottom\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\"\u003eClear Polycarbonate Top Cover\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\"\u003eAcrylic PCB Base Plate\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\"\u003eIP54 certified cable insert plugs\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×5\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eIP65 certified cable insert plugs\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×5\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eGrommet Frame\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\"\u003eAllen Key\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\"\u003ePhillips Screwdriver\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\"\u003e400mm Heavy Duty Zip Tie\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×8\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eM2.5x11mm Brass Standoff for board stacking\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×2\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eM2.5 Stainless Steel Flat Washer\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×8\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eM2.5 Stainless Steel Nut\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×8\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eM5x30mm Plastic Expansion Wall Plug with Screw\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\"\u003eM4x22mm Screw for top cover\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\"\u003eM3x9mm Screw for fixing the base plate to the case bottom\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\"\u003eM5x25mm Screw with nuts\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×2\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"sxd-box-n\"\u003eM2.5x10mm Screw for fixing Raspberry Pi to the base plate\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\"\u003eM2.5x8mm Screw for board stacking\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×4\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"sxd-fine\"\u003eThis enclosure is not assembled. Raspberry Pi and antennas are not included.\u003c\/p\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\"\u003eCompatible with\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eRaspberry Pi\u003c\/li\u003e\n\u003cli\u003eBeaglebone\u003c\/li\u003e\n\u003cli\u003eTinker Board\u003c\/li\u003e\n\u003cli\u003eStackable HATs\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Sixfab","offers":[{"title":"Default Title","offer_id":48944211689699,"sku":"S132","price":69.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0795\/4618\/8003\/files\/IP54-Enclosure.jpg?v=1778673760"},{"product_id":"raspberry-pi-base-hat-3g-4g-lte-minipcie-cards","title":"Sixfab 3G - 4G\/LTE Base HAT for Raspberry Pi","description":"\u003cdiv class=\"sxd\"\u003e\n\u003cdiv class=\"sxd-intro\"\u003e\n\u003cp class=\"sxd-lead\"\u003eThe Sixfab Base HAT is a versatile carrier board that serves as the foundation of your project. Separately add the exact Mini PCIe modem (CAT-M1, CAT-1, CAT-4, or 5G RedCap) and antennas that fit your specific requirements.\u003c\/p\u003e\n\u003cp\u003eIt bridges the gap between your hardware and robust cellular networks as an industrial mini PCIe interface, supporting global 3G, LTE CAT-M1, CAT 1, CAT 4 and even next-gen 5G RedCap connectivity to the edge. It mounts seamlessly to any standard form-factor Raspberry Pi (A+, B+, Pi 2, 3, 4, 5) or Pi Zero and is fully compatible with any Linux device featuring a similar 40-pin header. You can also connect the Base HAT to any Linux device or PC directly via USB (GPIO functionality is unavailable in USB-only mode).\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\u003e3G to 5G RedCap\u003c\/strong\u003e\u003cspan\u003eModem-dependent\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMini PCIe\u003c\/strong\u003e\u003cspan\u003eUSB 2.0 to host device\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFCC · CE · IC · RoHS\u003c\/strong\u003e\u003cspan\u003eRegulatory compliance\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\"\u003eSupported RATs\u003c\/th\u003e\n\u003ctd\u003e3G, LTE CAT M1, CAT 1, CAT 4, and 5G RedCap (modem-dependent)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eDevice compatibility\u003c\/th\u003e\n\u003ctd\u003eRaspberry Pi (40-pin models), 40-pin Linux SBCs, USB-equipped Linux devices\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePower input\u003c\/th\u003e\n\u003ctd\u003e5V DC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eOperating temp\u003c\/th\u003e\n\u003ctd\u003e-25 °C to 70 °C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eInterface\u003c\/th\u003e\n\u003ctd\u003eStandard Mini PCIe (USB 2.0 to host device)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eRegulatory compliance\u003c\/th\u003e\n\u003ctd\u003eFCC, CE, IC, RoHS\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\u003eVerified compatible modules\u003c\/h3\u003e\n\u003cdiv class=\"sxd-scroll\"\u003e\u003ctable class=\"sxd-table\"\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth scope=\"col\"\u003eOEM\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eCAT-M1 \/ NB-IoT\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eCAT 1\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eCAT 4\u003c\/th\u003e\n\u003cth scope=\"col\"\u003e5G RedCap\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eQuectel\u003c\/th\u003e\n\u003ctd\u003eBG95, BG96 Series\u003c\/td\u003e\n\u003ctd\u003eEG21-G, EC21 variants\u003c\/td\u003e\n\u003ctd\u003eEG25-G, EC25 variants\u003c\/td\u003e\n\u003ctd\u003eRG255C Series\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eTelit\u003c\/th\u003e\n\u003ctd\u003eME910C1-WW\u003c\/td\u003e\n\u003ctd\u003eLE910C1 Series\u003c\/td\u003e\n\u003ctd\u003eLE910C4, mPLS83 Series\u003c\/td\u003e\n\u003ctd\u003eFE910C04-NA, FE910C04-WW\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eSierra Wireless\u003c\/th\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003eMC7304, MC-WP7610\u003c\/td\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eSIMCom\u003c\/th\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003eSIM7600, A7602\u003c\/td\u003e\n\u003ctd\u003eSIM8230G, A8200C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eFibocom\u003c\/th\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003eL610 variants\u003c\/td\u003e\n\u003ctd\u003eNL668 variants\u003c\/td\u003e\n\u003ctd\u003eFG132-GL\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\u003c\/div\u003e\n\u003cp\u003eThis matrix covers the most commonly deployed modules, but the Base HAT is designed to support many others! Check the technical documentation to verify a specific Mini PCIe module.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eReal-world applications\u003c\/h3\u003e\n\u003cul class=\"sxd-dl\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eEdge AI \u0026amp; video surveillance (5G RedCap)\u003c\/strong\u003e\u003cspan\u003eDeploy low-latency, power-optimized connectivity for computer vision and industrial robotics.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMobile gateways \u0026amp; failover internet (CAT 4)\u003c\/strong\u003e\u003cspan\u003eProvide high-bandwidth CAT 4 connections for vehicle WiFi and remote branches.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFleet tracking \u0026amp; smart kiosks (CAT 1)\u003c\/strong\u003e\u003cspan\u003eUtilize cost-effective, medium-bandwidth capabilities for logistics and point-of-sale terminals.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRemote off-grid sensors (CAT M1)\u003c\/strong\u003e\u003cspan\u003eLeverage ultra-low power consumption and extended signal penetration for agricultural telemetry.\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\"\u003eSixfab 3G-4G\/LTE Base HAT for Raspberry Pi\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\"\u003eExtra tall 40-pin GPIO stacking header\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\"\u003eShort 40-pin GPIO Stacking Header\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\"\u003eRight Angle Micro USB Cable\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×1\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"sxd-fine\"\u003eMini PCIe modem modules and antenna are not included.\u003c\/p\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\n\u003cli\u003eRaspberry Pi A+, B+, Pi 2, 3, 4, 5\u003c\/li\u003e\n\u003cli\u003ePi Zero\u003c\/li\u003e\n\u003cli\u003eLinux devices with a similar 40-pin header\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAny Linux device or PC via USB\u003cspan class=\"sxd-host-s\"\u003eno GPIO\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Sixfab","offers":[{"title":"Default Title","offer_id":48944212705507,"sku":"S121","price":45.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0795\/4618\/8003\/files\/base-hat-main.jpg?v=1778673784"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0795\/4618\/8003\/collections\/Sixfab-AI-HAT-Plus-11_d7f69fff-496c-40d3-a9a6-08490a8d3081.jpg?v=1791479521","url":"https:\/\/eu.sixfab.com\/collections\/best-sellers.oembed","provider":"Sixfab","version":"1.0","type":"link"}