{"title":"Home page","description":"\u003cp\u003eThe Sixfab homepage. Featured products + categories.\u003c\/p\u003e","products":[{"product_id":"alpon-x5-ai","title":"ALPON X5 AI","description":"\u003cdiv class=\"sxd\"\u003e\n\u003cdiv class=\"sxd-intro\"\u003e\n\u003cp class=\"sxd-lead\"\u003eALPON X5 AI is a rugged, fanless edge AI computer powered by the Raspberry Pi Compute Module 5 and the DEEPX DX-M1 accelerator, delivering up to 25 TOPS of on-device AI inference.\u003c\/p\u003e\n\u003cp\u003eIt integrates NVMe SSD storage, dual Ethernet ports, 4G LTE\/eSIM connectivity and seamless integration with ALPON Cloud for remote management and OTA updates. Built for industrial, robotics or smart IoT applications, this next-generation edge device enables real-time AI processing directly on-site. Winner of the CES® 2026 Best of Innovation award.\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\u003eDEEPX DX-M1 NPU\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e16 GB\u003c\/strong\u003e\u003cspan\u003eLPDDR4X system memory\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e−20 → +60 °C\u003c\/strong\u003e\u003cspan\u003eFanless, rugged\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 NPU AI accelerator\u003c\/strong\u003e\u003cspan\u003eDEEPX DX-M1, 4 GB on-chip memory.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e16 GB system memory\u003c\/strong\u003e\u003cspan\u003eLPDDR4X with NVMe SSD support.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e2.4 GHz CPU clock\u003c\/strong\u003e\u003cspan\u003eQuad-core Cortex-A76.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLTE \/ Wi-Fi \/ 2× GbE\u003c\/strong\u003e\u003cspan\u003eNo external networking hardware required. Always-on industrial connectivity stack.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e−20 → 60 °C\u003c\/strong\u003e\u003cspan\u003eOperating range, validated.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e12-24 V DC\u003c\/strong\u003e\u003cspan\u003eIndustrial wide-range input.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eWatchdog\u003c\/strong\u003e\u003cspan\u003eReboots out of stuck states.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFleet management\u003c\/strong\u003e\u003cspan\u003eCentralized monitoring and control at scale.\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 class=\"sxd-group\"\u003e\u003cth colspan=\"2\"\u003eCompute\u003c\/th\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eAI accelerator\u003c\/th\u003e\n\u003ctd\u003eDEEPX DX-M1 · 25 TOPS · 4 GB on-chip memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eSystem memory\u003c\/th\u003e\n\u003ctd\u003e16 GB LPDDR4X · NVMe SSD support\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eCPU\u003c\/th\u003e\n\u003ctd\u003eQuad-core Cortex-A76 · 2.4 GHz (Raspberry Pi Compute Module 5)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr class=\"sxd-group\"\u003e\u003cth colspan=\"2\"\u003eConnectivity\u003c\/th\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eNetwork\u003c\/th\u003e\n\u003ctd\u003e4G LTE \/ eSIM · Wi-Fi · 2× Gigabit Ethernet\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eCloud\u003c\/th\u003e\n\u003ctd\u003eALPON Cloud - remote management, OTA updates and fleet monitoring\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr class=\"sxd-group\"\u003e\u003cth colspan=\"2\"\u003eIndustrial\u003c\/th\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eOperating temp\u003c\/th\u003e\n\u003ctd\u003e−20 °C to +60 °C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePower input\u003c\/th\u003e\n\u003ctd\u003e12-24 V DC wide-range input\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eReliability\u003c\/th\u003e\n\u003ctd\u003eWatchdog - reboots out of stuck states\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr class=\"sxd-group\"\u003e\u003cth colspan=\"2\"\u003eRegulatory\u003c\/th\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eCertification\u003c\/th\u003e\n\u003ctd\u003eCertification in progress: CE, FCC, IC, UKCA, RoHS, WEEE, 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\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\u003ca href=\"\/products\/ai-hat-plus-raspberry-pi-5\"\u003eAI HAT+ for Raspberry Pi 5\u003c\/a\u003e\u003cspan\u003e13 \/ 25 TOPS\u003c\/span\u003e\n\u003c\/div\u003e\n\u003cp\u003eAI acceleration for Raspberry Pi 5. Up to 25 TOPS of local AI performance.\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\"\u003eEdge 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.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv class=\"sxd-row-h\"\u003e\n\u003cstrong\u003eALPON X5 AI\u003c\/strong\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\u003eHow it compares\u003c\/h3\u003e\n\u003cdiv class=\"sxd-scroll\"\u003e\u003ctable class=\"sxd-table\"\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth scope=\"col\"\u003eFeature\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eAI development boards\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eIndustrial AI PCs\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eALPON X5 AI\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eAI inference \/ compute\u003c\/th\u003e\n\u003ctd\u003eLimited TOPS, varies by board\u003c\/td\u003e\n\u003ctd\u003e25 TOPS NPU\u003c\/td\u003e\n\u003ctd\u003e25 TOPS NPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eBuilt-in LTE connectivity\u003c\/th\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003eBuilt-in, no external modem\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eCloud fleet management\u003c\/th\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003eIncluded, no third-party SaaS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eZero-touch provisioning \u0026amp; OTA\u003c\/th\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003e✓\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eFanless rugged design\u003c\/th\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003ePartial\u003c\/td\u003e\n\u003ctd\u003e−20 °C to +60 °C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eLow-power AI (vs GPU)\u003c\/th\u003e\n\u003ctd\u003eHigh power, active cooling needed\u003c\/td\u003e\n\u003ctd\u003eGPU-based (high power)\u003c\/td\u003e\n\u003ctd\u003eNPU, no active cooling\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePoC to production\u003c\/th\u003e\n\u003ctd\u003ePoC only, not production-ready\u003c\/td\u003e\n\u003ctd\u003e-\u003c\/td\u003e\n\u003ctd\u003ePurpose-built\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eOpen \/ extensible architecture\u003c\/th\u003e\n\u003ctd\u003eLimited\u003c\/td\u003e\n\u003ctd\u003eLimited\u003c\/td\u003e\n\u003ctd\u003eRaspberry Pi CM5 + GPIO\/USB expansion\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\u003c\/div\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\u003eManufacturing\u003c\/strong\u003e\u003cspan\u003eDetect defects on a conveyor using a single industrial camera in real time. Train YOLOv8 with Ultralytics, compile for DEEPX NPU, and deploy via ALPON Cloud OTA updates.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSmart cities\u003c\/strong\u003e\u003cspan\u003eCount and classify vehicles, cyclists, and pedestrians from roadside edge units. Aggregate metadata over cellular networks while keeping raw video fully on-device.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eInfrastructure\u003c\/strong\u003e\u003cspan\u003eMonitor pumps, switchgear, and substations in unattended environments. Run anomaly detection locally and transmit only event-based alerts over LTE.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMaintenance\u003c\/strong\u003e\u003cspan\u003eAnalyze multimodal industrial signals to model normal operating behavior and detect statistical deviations in real time. Infer failure probability locally without streaming raw data.\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\"\u003eALPON X5 AI device (25 TOPS · INT8)\u003cspan class=\"sxd-box-s\"\u003eFanless aluminum enclosure with the DEEPX DX-M1 NPU and Raspberry Pi CM5 integrated; nothing to assemble inside. Four external SMA connectors on the back panel are labeled L, L, G and W.\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\"\u003eExternal antennas: 2× LTE, 1× GNSS, 1× Wi-Fi (labels match the connectors)\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\"\u003e27 W USB-C PD adapter with four interchangeable plug heads (US \/ EU \/ UK \/ AU)\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e×1\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch4 class=\"sxd-sub\"\u003eNot included (sold separately)\u003c\/h4\u003e\n\u003cp class=\"sxd-ni-p\"\u003ewall mount bracket · DIN rail mount kit (35 mm per EN 60715) · IP67 outdoor antennas · DC terminal power source (12-24 V DC)\u003c\/p\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 AI workloads does ALPON X5 AI support?\u003c\/summary\u003e\u003cp\u003eALPON X5 AI is optimized for real-time Vision AI workloads, including object detection, classification, segmentation, anomaly detection, OCR, and pose estimation, running production-grade models such as YOLO and ResNet optimized for the DEEPX DX-M1 NPU. While it is not currently focused on LLM inference, this capability is on the DEEPX roadmap and will be supported as the underlying silicon ecosystem evolves, with Sixfab extending support accordingly.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes it require cloud connectivity for AI inference?\u003c\/summary\u003e\u003cp\u003eNo. ALPON X5 AI runs AI inference fully on-device. Cloud connectivity via ALPON Cloud is optional and used for remote management, OTA updates, and fleet monitoring, not for real-time inference.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs it suitable for industrial environments?\u003c\/summary\u003e\u003cp\u003eYes. ALPON X5 AI is designed for industrial deployment with support for wide temperature ranges, sealed enclosure options, and stable operation under continuous 24\/7 workloads.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat is the difference between an NPU and a GPU?\u003c\/summary\u003e\u003cp\u003eA GPU is optimized for parallel processing and commonly used for AI training, but it consumes more power and requires active cooling. An NPU is designed specifically for inference, delivering higher efficiency, lower power consumption, and fanless operation - making it more suitable for industrial edge deployments.\u003c\/p\u003e\u003c\/details\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Sixfab","offers":[{"title":"Default Title","offer_id":48916962476259,"sku":"S161","price":700.0,"currency_code":"EUR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0795\/4618\/8003\/files\/Sixfab-ALPON-X5-AI-2.jpg?v=1778161779"},{"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-5g-development-kit-5g-hat","title":"Sixfab 5G Development Kit v2 for Raspberry Pi","description":"\u003cdiv class=\"sxd\"\u003e\n\u003cdiv class=\"sxd-intro\"\u003e\n\u003cp class=\"sxd-lead\"\u003eThe Sixfab 5G Development Kit v2 for Raspberry Pi brings ultra-high-speed 5G connectivity to your Raspberry Pi 5 and Raspberry Pi 4 projects. It comes as a single, highly flexible base platform without an M.2 module pre-installed, so you can add a Quectel RM520N-GL or bring your own compatible 5G\/4G module.\u003c\/p\u003e\n\u003cul class=\"sxd-list\"\u003e\n\u003cli\u003eHardware designed for Raspberry Pi 5 and 4\u003c\/li\u003e\n\u003cli\u003eCompatible with the Quectel RM520N-GL M.2 5G module\u003c\/li\u003e\n\u003cli\u003eEnclosure, antennas and USB bridges are included\u003c\/li\u003e\n\u003cli\u003eDevelopment friendly, modification-ready hardware\u003c\/li\u003e\n\u003cli\u003eThe HAT can also be used via USB as a standalone dongle\u003c\/li\u003e\n\u003cli\u003eMiddleware compatible via USB with Linux-based embedded platforms\u003c\/li\u003e\n\u003c\/ul\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\u003eRaspberry Pi 5 \/ 4\u003c\/strong\u003e\u003cspan\u003eDesigned for both\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNano SIM\u003c\/strong\u003e\u003cspan\u003eAccessible on top of the HAT\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eM.2 5G slot\u003c\/strong\u003e\u003cspan\u003eQuectel RM520N-GL compatible\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\u003eAll-in-one development\u003c\/strong\u003e\u003cspan\u003eIncludes a project enclosure, high-performance SMA antennas, cooling fan, cellular module heatsinks and a 5V Type-C power supply (minus the Pi and modem).\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRaspberry Pi compatibility\u003c\/strong\u003e\u003cspan\u003eFully compatible with Raspberry Pi 5 and Raspberry Pi 4.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStandalone capable\u003c\/strong\u003e\u003cspan\u003eThe modification-ready HAT can be used as a standalone 5G dongle via USB with Linux-based embedded platforms, including NVIDIA Jetson Developer Kits, Raspberry Pi, NXP i.MX platforms, and standard Linux \u0026amp; Windows PCs.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOptimized thermal management\u003c\/strong\u003e\u003cspan\u003eCooling fan, dedicated heatsinks and a ventilated enclosure keep the modem and Pi cool.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConfiguration ready\u003c\/strong\u003e\u003cspan\u003eBoth the enclosure and the base plate allow hardware modification.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSimple setup\u003c\/strong\u003e\u003cspan\u003eEasy-to-follow tutorials to get connected.\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 new in the kit\u003c\/h3\u003e\n\u003cul class=\"sxd-dl\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eRaspberry Pi 5 ready\u003c\/strong\u003e\u003cspan\u003eBuilt to handle the enhanced power and data demands of the newest Pi, and still perfectly compatible with Pi 4.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNano SIM slot\u003c\/strong\u003e\u003cspan\u003eUpgraded from Micro to Nano SIM for modern SIM card compatibility.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sxd-sec\"\u003e\n\u003ch3\u003eBuild it your way\u003c\/h3\u003e\n\u003cp\u003eThe 5G Development Kit comes as a single, highly flexible base platform without an M.2 module pre-installed.\u003c\/p\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\u003eBundle and save\u003c\/h4\u003e\n\u003cp\u003eSelect an available M.2 module such as the \u003ca href=\"\/products\/quectel-rm520n-gl-5g-module\"\u003eQuectel RM520N-GL 5G Module\u003c\/a\u003e.\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\u003eBring your own\u003c\/h4\u003e\n\u003cp\u003eUse a compatible M.2 5G\/4G module of your choice.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\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\u003e5G\/4G network tests, IoT, and eMBB applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eData speeds\u003c\/th\u003e\n\u003ctd\u003eDepends on module \u0026amp; carrier\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eCompatibility\u003c\/th\u003e\n\u003ctd\u003eRaspberry Pi 5, Raspberry Pi 4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eSIM interface\u003c\/th\u003e\n\u003ctd\u003eNano SIM (easily accessible on top of the HAT)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePositioning\u003c\/th\u003e\n\u003ctd\u003eGPS, GNSS (depending on paired M.2 module)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eElectrical\u003c\/th\u003e\n\u003ctd\u003e5V DC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eOperating temperature\u003c\/th\u003e\n\u003ctd\u003e-25 °C to 70 °C\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 5G HAT v2 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\"\u003ePlastic enclosure for Raspberry Pi 5G projects\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\"\u003e5G\/LTE Cellular Omni-Directional Antenna\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\"\u003e5V DC – 3A Type-C Power Supply with US, UK, EU, AU plugs\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 Connector for Raspberry Pi 5\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 Connector for Raspberry Pi 4\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\"\u003eCooling fan and antenna adapters (installed on the enclosure)\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\"\u003eHeatsink for M.2 module\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\"\u003eScrews and standoffs\u003c\/span\u003e\u003cspan class=\"sxd-box-q\"\u003e1 set\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"sxd-fine\"\u003eRequires a Raspberry Pi 5 or 4 and an M.2 5G module (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 hosts\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eRaspberry Pi 5\u003c\/li\u003e\n\u003cli\u003eRaspberry Pi 4\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\"\u003eM.2 module\u003c\/th\u003e\n\u003ctd\u003e\u003cspan class=\"sxd-item\"\u003e\u003ca href=\"\/products\/quectel-rm520n-gl-5g-module\"\u003eQuectel RM520N-GL M.2 5G Module\u003c\/a\u003e\u003c\/span\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eVia USB\u003c\/th\u003e\n\u003ctd\u003e\n\u003cspan class=\"sxd-item\"\u003eLinux-based embedded platforms\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003eNVIDIA Jetson Developer Kits\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003eRaspberry Pi\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003eNXP i.MX platforms\u003c\/span\u003e\u003cspan class=\"sxd-sep\"\u003e · \u003c\/span\u003e\u003cspan class=\"sxd-item\"\u003eStandard Linux \u0026amp; Windows PCs\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\u003eGetting started\u003c\/h3\u003e\n\u003cul class=\"sxd-list\"\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/docs.sixfab.com\/docs\/raspberry-pi-5g-development-kit-v2-getting-started\"\u003eDocumentation and getting started guide\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/docs.sixfab.com\/docs\/raspberry-pi-5g-development-kit-faq\"\u003eFrequently asked questions\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003ca href=\"\/pages\/contact\"\u003eContact sales\u003c\/a\u003e for volume orders\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Sixfab","offers":[{"title":"Default Title","offer_id":48944211984611,"sku":"B81","price":195.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0795\/4618\/8003\/files\/5G_Dev_kit_v2_1.png?v=1790862142"},{"product_id":"connect-sim-card-for-iot-projects","title":"Sixfab SIM","description":"\u003cdiv class=\"sxd\"\u003e\n\u003cdiv class=\"sxd-intro\"\u003e\n\u003cp class=\"sxd-lead\"\u003eThe Sixfab SIM is a single multi-IMSI SIM card that provides extensive worldwide coverage across over 344 top-tier global 2G, 3G, 4G LTE, LTE-M network carriers in over 174 countries, with automatic network failover and redundancy - no user intervention required.\u003c\/p\u003e\n\u003cp\u003eIt features all sizes (2FF, 3FF, 4FF), allowing you to effortlessly extract the desired size using the perforated lines. Control registration, activation, deactivation and check usage stats through the web console and API.\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\u003e344+ carriers\u003c\/strong\u003e\u003cspan\u003e174+ countries\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e$2.00\/month\u003c\/strong\u003e\u003cspan\u003ePer active SIM · inactive SIMs incur no costs\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e2FF \/ 3FF \/ 4FF\u003c\/strong\u003e\u003cspan\u003eMulti-size SIM\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\u003eGlobal coverage\u003c\/strong\u003e\u003cspan\u003eOver 344 top-tier carriers in over 174 countries.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAutomatic failover\u003c\/strong\u003e\u003cspan\u003eNetwork failover and redundancy, no user intervention required.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAll sizes\u003c\/strong\u003e\u003cspan\u003e2FF, 3FF, 4FF with perforated lines.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eManagement\u003c\/strong\u003e\u003cspan\u003eRegistration, activation, deactivation and usage stats via web console and API.\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 class=\"sxd-group\"\u003e\u003cth colspan=\"2\"\u003eNetwork\u003c\/th\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eSupported\u003c\/th\u003e\n\u003ctd\u003e2G, 3G, 4G LTE, LTE-M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eNot supported\u003c\/th\u003e\n\u003ctd\u003eNB-IoT, voice calls, SMS, static IP\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr class=\"sxd-group\"\u003e\u003cth colspan=\"2\"\u003eAPN configuration\u003c\/th\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eAPN\u003c\/th\u003e\n\u003ctd\u003esuper\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePIN\u003c\/th\u003e\n\u003ctd\u003e(None)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eUsername\u003c\/th\u003e\n\u003ctd\u003e(None)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePassword\u003c\/th\u003e\n\u003ctd\u003e(None)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr class=\"sxd-group\"\u003e\u003cth colspan=\"2\"\u003ePricing\u003c\/th\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eHardware\u003c\/th\u003e\n\u003ctd\u003e$3.00 per SIM (discounts available for bulk orders)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eSubscription\u003c\/th\u003e\n\u003ctd\u003e$2.00\/month\/SIM for active cards; inactive SIMs incur no costs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003ePay-as-you-go data\u003c\/th\u003e\n\u003ctd\u003e$0.1\/MB for most countries\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\"\u003eRegional data pools\u003c\/th\u003e\n\u003ctd\u003eFrom $9-$15 (500MB) to $990-$1,620 (100GB)\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 multi-size (2FF\/3FF\/4FF) SIM Card\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\u003c\/div\u003e","brand":"Sixfab Connect","offers":[{"title":"Default Title","offer_id":48944213229795,"sku":"SM4","price":3.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0795\/4618\/8003\/files\/Sixfab-SIM-PP.png?v=1778673910"}],"url":"https:\/\/eu.sixfab.com\/collections\/frontpage.oembed","provider":"Sixfab","version":"1.0","type":"link"}