{"product_id":"aio-8550jd4-qcs8550-ai-motherboard","title":"AIO-8550JD4 QCS8550 AI Motherboard","description":"\u003cp class=\"geo-summary\" style=\"font-size:1rem;line-height:1.65;margin:0 0 1.25rem;padding:0 0 1rem;border-bottom:1px solid #e5e7eb;color:#1f2937\"\u003e\u003cstrong\u003eAIO-8550JD4 QCS8550 AI Motherboard\u003c\/strong\u003e is a Firefly product sold by Ai-Paipai. Key specifications — Soc: Qualcomm QCS8550 · Npu: Dual eNPU V3: Equipped with 4 HVX (Hexagon Vector… · Ram: 16GB LPDDR5x · Storage: 256GB UFS4.0 · Network: Ethernet: 2 × RJ45(1000Mbps) WiFi: Extend WiFi\/Bluetooth…. Typical applications: edge AI inference, machine vision and IoT gateway deployments. Ai-Paipai (Shenzhen AiPaipai Intelligent Technology Co., Ltd.) is an authorized distributor providing local technical support, pre-installed SDK \/ OS images, OEM \/ ODM customization, and worldwide shipping.\u003c\/p\u003e \u003c!-- Firefly product tabs --\u003e \u003cdiv class=\"fx-tabs\" data-fx-tabs\u003e \u003cdiv class=\"fx-tablist\" role=\"tablist\"\u003e \u003cbutton class=\"fx-tab is-active\" role=\"tab\" data-fx-tab=\"0\"\u003eDescription\u003c\/button\u003e \u003cbutton class=\"fx-tab\" role=\"tab\" data-fx-tab=\"1\"\u003eWhat's Included\u003c\/button\u003e \u003cbutton class=\"fx-tab\" role=\"tab\" data-fx-tab=\"2\"\u003eSpecifications\u003c\/button\u003e \u003cbutton class=\"fx-tab\" role=\"tab\" data-fx-tab=\"3\"\u003eDocuments\u003c\/button\u003e \u003cbutton class=\"fx-tab\" role=\"tab\" data-fx-tab=\"4\"\u003eShipping Information\u003c\/button\u003e \u003cbutton class=\"fx-tab\" role=\"tab\" data-fx-tab=\"5\"\u003eWhy Buy From Ai-Paipai?\u003c\/button\u003e \u003c\/div\u003e \u003cdiv class=\"fx-panel is-active\" data-fx-panel=\"0\"\u003e \u003cdiv style=\"border:2px solid #2b6cb0;background:#f0f7ff;padding:14px 18px;margin:0 0 18px;border-radius:8px;\"\u003e \u003ch3 style=\"margin:0 0 6px;color:#2b6cb0;\"\u003e✅ Authorized Distributor — Ai-Paipai (Shenzhen AiPaipai Intelligent Technology Co., Ltd.)\u003c\/h3\u003e \u003cp style=\"margin:0;font-size:14px;\"\u003eThis product is supplied by an \u003cb\u003eauthorized distributor\u003c\/b\u003e of the original brand.\nWe provide \u003cb\u003elocal technical support, pre-installed SDK \/ mirror images, and OEM \/ ODM customization\u003c\/b\u003e, plus fast global shipping.\u003c\/p\u003e \u003c\/div\u003e \u003cp\u003eThe AIO-8550JD4 is powered by a Qualcomm QCS8550 octa-core AI processor with a 48-tops NPU, supporting mainstream AI models and frameworks. It also features an Adreno 740 GPU for ray tracing and 8K video processing; three cognitive ISPs supporting cameras up to 100MP; and various expansion ports. AI optimization tools and reference designs are also included to facilitate efficient development.\u003c\/p\u003e \u003cul\u003e \u003cli style=\"font-weight: bold;\"\u003e \u003cstrong\u003eProduct Datasheet: \u003ca href=\"https:\/\/download.t-firefly.com\/Spec\/Mainboards\/AIO-8550JD4_Specification_EN.pdf\" target=\"_blank\" rel=\"noopener\"\u003eClick Here\u003c\/a\u003e\u003c\/strong\u003e\u003cbr\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003ch2 style=\"font-size: 23px;\"\u003e\u003cstrong\u003eFeature\u003c\/strong\u003e\u003c\/h2\u003e \u003cul\u003e \u003cli\u003e \u003cstrong\u003eAI Processor QCS8550:\u003c\/strong\u003e The QCS8550 uses an octa-core Kryo CPU based on the ARM architecture with a clock speed of up to 3.36 GHz.\u003c\/li\u003e \u003cli\u003e \u003cstrong\u003eDeployment of Large AI Models:\u003c\/strong\u003e Supports large-scale private deployment of Transformer models.\u003c\/li\u003e \u003cli\u003e \u003cstrong\u003eMultiple Deep Learning Frameworks:\u003c\/strong\u003e Supports the QNN AI inference framework, as well as various deep learning frameworks, including TensorFlow, TensorFlow Lite, PyTorch, and ONNX.\u003c\/li\u003e \u003cli\u003e \u003cstrong\u003eSupports Ray Tracing Technology:\u003c\/strong\u003e It integrates an Adreno 740 GPU, supports ray tracing technology, and is compatible with full profiles for OpenGL ES 3.2, Vulkan 1.2, and OpenCL 3.0. Combined with Hexagon direct neural acceleration technology, it delivers enhanced graphics processing and AI computing performance.\u003c\/li\u003e \u003cli\u003e \u003cstrong\u003eQualcomm Cognitive ISP with 36MP Triple Camera:\u003c\/strong\u003e Features the Qualcomm Spectra Cognitive ISP with triple 18-bit ISPs, supporting camera configurations up to a triple 36MP, dual 64MP+36MP, or a single 108MP setup.\u003c\/li\u003e \u003c\/ul\u003e \u003ch2 style=\"font-size: 23px;\"\u003e\u003cstrong\u003eProduct Overview\u003c\/strong\u003e\u003c\/h2\u003e \u003cdiv style=\"text-align: center;\"\u003e\u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0592\/4965\/5892\/files\/1_05_1.jpg?v=1785401167\" alt=\"\"\u003e\u003c\/div\u003e \u003chr\u003e \u003ch2\u003e \u003c\/h2\u003e \u003c\/div\u003e \u003cdiv class=\"fx-panel\" data-fx-panel=\"1\"\u003e \u003ch3\u003eShipping list\u003c\/h3\u003e \u003cul\u003e \u003cli\u003eMain unit × 1\u003c\/li\u003e \u003cli\u003ePower adapter \/ cable (region plug on request) × 1\u003c\/li\u003e \u003cli\u003eAccessories vary by configuration — contact us for the exact included list.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/div\u003e \u003cdiv class=\"fx-panel\" data-fx-panel=\"2\"\u003e \u003cdiv class=\"fx-specs\"\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eSoc\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eQualcomm QCS8550\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eCpu\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eQualcomm Kryo® CPU: Octa-core 64-bit (1×GoldPlus@3.2GHz + (2+2)Gold@2.8GHz + 3×Silver@2.0GHz), 4nm advanced process, maximum frequency up to 3.36GHz\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eGpu\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eAdreno 740 GPU: Supports ray tracing technology, OpenGL ES 3.2, Vulkan 1.2, full-profile OpenCL 3.0, and Adreno NN Direct\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eNpu\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eDual eNPU V3: Equipped with 4 HVX (Hexagon Vector Extensions), 1 HMX (Hexagon Matrix Extension); Computing power up to 48 TOPS (INT8), 12 TOPS (FP16)\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eRam\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003e16GB LPDDR5x\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eStorage\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003e256GB UFS4.0\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eStorage expansion\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003e1 × M.2 M-KEY (supports expansion of SATA 3.0\/PCIe NVMe SSD, compatible with 2242\/2260\/2280 form factors), 1 × TF Card\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eCodecs\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eVideo decoding: 8K@60fps\/4K@240fps H.264\/VP9\/AV1\nVideo encoding: 8K@30fps\/4K@120fps H.264\nSupports concurrent 4K@60fps decoding and 4K@60fps encoding for wireless display scenarios\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003ePower\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eDC 12V (5.5mm × 2.1mm, support 12V~24V wide voltage input)\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003ePower consumption\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eNormal: 4.8W(12V\/400mA), Max: 18W(12V\/1500mA)\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eVideo output\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003e1 × HDMI2.0 (4K@60Hz)\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eVideo input\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003e2 × MIPI D\/C PHY (4 lanes DPHY or 3 trios CPHY) + 2 × MIPI D\/C PHY (2 lanes DPHY or 1 trio CPHY)\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eAudio\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003e1 × 3.5mm Audio jack (Supports MIC recording, American standard CTIA)\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eOs\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eLinux\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eSoftware support\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eSupports on-premises deployment of large-scale parameter models based on the Transformer architecture, such as large language models (LLMs) including the Deepseek-R1 series, Gemma series, Llama series, Qwen series, Phi series, etc.\nSupports the QNN AI inference framework, as well as various deep learning frameworks including TensorFlow, TensorFlow Lite, PyTorch, ONNX, etc.\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eDimension\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003e122.89mm × 85.04mm × 31.55mm\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eWeight\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eWithout heatsink: 117g; With heatsink: 165g\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eEnvironment\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eOperating Temperature: -20℃~60℃, Storage Temperature: -20℃~70℃, Operating Humidity: 10%~90%RH (No condensation)\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eNetwork\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003eEthernet: 2 × RJ45(1000Mbps)\nWiFi: Extend WiFi\/Bluetooth module through M.2 E-KEY (2230), supporting 2.4GHz\/5GHz dual band WiFi6 (802.11a\/b\/g\/n\/ac\/\nax) and Bluetooth 5.2\n4G: Extend 4G LTE via Mini PCIe (Shared with 5G)\n5G: Extend 5G via M.2 B-KEY (Shared with 4G, not mounted by default)\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eUsb\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003e2 × USB3.0 (Max: 1A), 1 × Type-C (Flash)\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eButton\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003e1 × Reset, 1 × Recovery, 1 × Power\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-spec\"\u003e \u003cdiv class=\"fx-k\"\u003eOther\u003c\/div\u003e \u003cdiv class=\"fx-v\"\u003e1 × FAN (4Pin-1.25mm), 1 × SIM Card\n1 × Double-row pin headers (20Pin-2.0mm): USB2.0, UART, 2 × I2C, Line in, Line out, GPIO\n1 × Phoenix connector (8Pin-3.5mm): 1 × RS485, 1 × RS232, 1 × CAN 2.0\nDIP switch:\n1 × Auto Power-on DIP Switch (ON: Power on directly when connected; 1(OFF): Press Power button briefly to power on after connection)\n1 × Type-C function selection(ON: Debug; 1(OFF): Flash)\u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"fx-panel\" data-fx-panel=\"3\"\u003e \u003cdiv class=\"fx-docs\"\u003e \u003cdiv class=\"fx-doc\"\u003e \u003cspan\u003eProduct tutorials\u003c\/span\u003e\u003ca href=\"#\" target=\"_blank\" rel=\"noopener\"\u003eClick Here\u003c\/a\u003e \u003c\/div\u003e \u003cdiv class=\"fx-doc\"\u003e \u003cspan\u003eProduct datasheet\u003c\/span\u003e\u003ca href=\"#\" target=\"_blank\" rel=\"noopener\"\u003eClick Here\u003c\/a\u003e \u003c\/div\u003e \u003cdiv class=\"fx-doc\"\u003e \u003cspan\u003eSDK software and firmware files\u003c\/span\u003e\u003ca href=\"#\" target=\"_blank\" rel=\"noopener\"\u003eClick Here\u003c\/a\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cp class=\"fx-note\"\u003eDocument links will be replaced with the official Firefly resource URLs on request.\u003c\/p\u003e \u003c\/div\u003e \u003cdiv class=\"fx-panel\" data-fx-panel=\"4\"\u003e \u003cp\u003e\u003cb\u003eOur cooperating carriers:\u003c\/b\u003e FedEx, DHL, UPS, SF Express\u003c\/p\u003e \u003ch3\u003eDelivery Area And Cost\u003c\/h3\u003e \u003cdiv class=\"fx-scroll\"\u003e \u003ctable class=\"fx-table\"\u003e \u003cthead\u003e\u003ctr\u003e \u003cth\u003eCountry\/Region\u003c\/th\u003e \u003cth\u003e0–2kg\u003c\/th\u003e \u003cth\u003e2–3kg\u003c\/th\u003e \u003cth\u003e3–4kg\u003c\/th\u003e \u003cth\u003e4–5kg\u003c\/th\u003e \u003cth\u003e5–6kg\u003c\/th\u003e \u003cth\u003e6–7kg\u003c\/th\u003e \u003cth\u003e7–8kg\u003c\/th\u003e \u003cth\u003e8–9kg\u003c\/th\u003e \u003cth\u003e9–10kg\u003c\/th\u003e \u003c\/tr\u003e\u003c\/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd\u003eJapan, Taiwan, Hong Kong, Macau\u003c\/td\u003e \u003ctd\u003e$15\u003c\/td\u003e \u003ctd\u003e$20\u003c\/td\u003e \u003ctd\u003e$25\u003c\/td\u003e \u003ctd\u003e$30\u003c\/td\u003e \u003ctd\u003e$35\u003c\/td\u003e \u003ctd\u003e$40\u003c\/td\u003e \u003ctd\u003e$45\u003c\/td\u003e \u003ctd\u003e$50\u003c\/td\u003e \u003ctd\u003e$55\u003c\/td\u003e \u003c\/tr\u003e \u003ctr\u003e \u003ctd\u003eSingapore, Thailand, Malaysia\u003c\/td\u003e \u003ctd\u003e$20\u003c\/td\u003e \u003ctd\u003e$25\u003c\/td\u003e \u003ctd\u003e$30\u003c\/td\u003e \u003ctd\u003e$35\u003c\/td\u003e \u003ctd\u003e$40\u003c\/td\u003e \u003ctd\u003e$45\u003c\/td\u003e \u003ctd\u003e$50\u003c\/td\u003e \u003ctd\u003e$55\u003c\/td\u003e \u003ctd\u003e$60\u003c\/td\u003e \u003c\/tr\u003e \u003ctr\u003e \u003ctd\u003eIndonesia, Philippines\u003c\/td\u003e \u003ctd\u003e$25\u003c\/td\u003e \u003ctd\u003e$30\u003c\/td\u003e \u003ctd\u003e$40\u003c\/td\u003e \u003ctd\u003e$50\u003c\/td\u003e \u003ctd\u003e$60\u003c\/td\u003e \u003ctd\u003e$70\u003c\/td\u003e \u003ctd\u003e$80\u003c\/td\u003e \u003ctd\u003e$85\u003c\/td\u003e \u003ctd\u003e$95\u003c\/td\u003e \u003c\/tr\u003e \u003ctr\u003e \u003ctd\u003eSouth Korea\u003c\/td\u003e \u003ctd\u003e$25\u003c\/td\u003e \u003ctd\u003e$35\u003c\/td\u003e \u003ctd\u003e$35\u003c\/td\u003e \u003ctd\u003e$45\u003c\/td\u003e \u003ctd\u003e$45\u003c\/td\u003e \u003ctd\u003e$55\u003c\/td\u003e \u003ctd\u003e$55\u003c\/td\u003e \u003ctd\u003e$65\u003c\/td\u003e \u003ctd\u003e$65\u003c\/td\u003e \u003c\/tr\u003e \u003ctr\u003e \u003ctd\u003eUnited States, Australia, New Zealand, Canada, UK, and most of Western Europe\u003c\/td\u003e \u003ctd\u003e$30\u003c\/td\u003e \u003ctd\u003e$45\u003c\/td\u003e \u003ctd\u003e$55\u003c\/td\u003e \u003ctd\u003e$65\u003c\/td\u003e \u003ctd\u003e$75\u003c\/td\u003e \u003ctd\u003e$85\u003c\/td\u003e \u003ctd\u003e$95\u003c\/td\u003e \u003ctd\u003e$105\u003c\/td\u003e \u003ctd\u003e$115\u003c\/td\u003e \u003c\/tr\u003e \u003ctr\u003e \u003ctd\u003eOther regions (Eastern Europe, Middle East, Latin America, etc.)\u003c\/td\u003e \u003ctd\u003e$50\u003c\/td\u003e \u003ctd\u003e$65\u003c\/td\u003e \u003ctd\u003e$80\u003c\/td\u003e \u003ctd\u003e$95\u003c\/td\u003e \u003ctd\u003e$110\u003c\/td\u003e \u003ctd\u003e$125\u003c\/td\u003e \u003ctd\u003e$135\u003c\/td\u003e \u003ctd\u003e$150\u003c\/td\u003e \u003ctd\u003e$165\u003c\/td\u003e \u003c\/tr\u003e \u003c\/tbody\u003e \u003c\/table\u003e \u003c\/div\u003e \u003cp class=\"fx-note\"\u003eShipping time is for reference only; the actual situation shall prevail.\u003c\/p\u003e \u003c\/div\u003e \u003cdiv class=\"fx-panel\" data-fx-panel=\"5\"\u003e\n Why Buy From Ai-Paipai? \u003cul\u003e \u003cli\u003e \u003cb\u003eAuthorized \u0026amp; Genuine\u003c\/b\u003e — sourced directly from brand-approved channels.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eLocal Engineering Support\u003c\/b\u003e — datasheets, SDKs, and pre-flashed images in English and Chinese.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eOEM \/ ODM \u0026amp; FPGA Customization\u003c\/b\u003e — tailor specs, enclosures, and BSP for your project.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eOne-Stop Sourcing\u003c\/b\u003e — Firefly + youyeetoo + our own FPGA \/ edge lineup in one store.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cstyle\u003e.fx-tabs{margin:24px 0;border:1px solid #e5e7eb;border-radius:10px;overflow:hidden;background:#fff}.fx-tablist{display:flex;flex-wrap:wrap;gap:0;border-bottom:1px solid #e5e7eb;background:#f8fafc}.fx-tab{flex:1 1 auto;min-width:120px;padding:14px 16px;border:0;background:transparent;cursor:pointer;font:600 14px\/1.3 inherit;color:#475569;border-bottom:3px solid transparent;transition:.18s}.fx-tab:hover{color:#0f172a;background:#eef2f7}.fx-tab.is-active{color:#0f172a;border-bottom-color:#2563eb;background:#fff}.fx-panel{display:none;padding:22px 24px}.fx-panel.is-active{display:block}.fx-panel h3{font-size:16px;margin:22px 0 8px;font-weight:700}.fx-panel h3:first-child{margin-top:0}.fx-panel p{margin:10px 0;line-height:1.65}.fx-panel ul{margin:10px 0;padding-left:22px}.fx-panel li{margin:6px 0;line-height:1.6}.fx-auth{background:#f0fdf4;border:1px solid #bbf7d0;border-radius:8px;padding:12px 14px;margin:0 0 16px;font-size:14px;line-height:1.6}.fx-specs{border-top:1px solid #eef2f7}.fx-spec{display:grid;grid-template-columns:230px 1fr;gap:16px;padding:12px 4px;border-bottom:1px solid #eef2f7}.fx-k{font-weight:700;color:#334155}.fx-v{color:#475569}.fx-docs{display:flex;flex-direction:column;gap:10px}.fx-doc{display:flex;justify-content:space-between;align-items:center;gap:12px;padding:12px 14px;border:1px solid #e5e7eb;border-radius:8px}.fx-doc a{color:#2563eb;font-weight:700;text-decoration:none}.fx-doc a:hover{text-decoration:underline}.fx-scroll{overflow-x:auto}.fx-table{border-collapse:collapse;width:100%;min-width:760px;font-size:13px}.fx-table th,.fx-table td{border:1px solid #e5e7eb;padding:8px 10px;text-align:left}.fx-table th{background:#f8fafc;font-weight:700}.fx-note{color:#64748b;font-size:13px;margin-top:12px}\n @media (max-width:749px){.fx-spec{grid-template-columns:1fr;gap:4px}.fx-tab{min-width:50%;flex:1 1 50%}.fx-panel{padding:16px}} \u003c\/style\u003e \u003cscript\u003e\n (function () {\n var roots = document.querySelectorAll('[data-fx-tabs]');\n roots.forEach(function (root) {\n var tabs = root.querySelectorAll('[data-fx-tab]');\n var panels = root.querySelectorAll('[data-fx-panel]');\n tabs.forEach(function (t) {\n t.addEventListener('click', function () {\n var i = t.getAttribute('data-fx-tab');\n tabs.forEach(function (x) { x.classList.toggle('is-active', x === t); });\n panels.forEach(function (p) { p.classList.toggle('is-active', p.getAttribute('data-fx-panel') === i); });\n });\n });\n });\n })(); \u003c\/script\u003e","brand":"Firefly","offers":[{"title":"16G RAM+256G ROM","offer_id":48422468485378,"sku":"firefly-aio-8550jd4-qcs8550-ai-motherboard-42924107563092","price":809.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0837\/5953\/0242\/files\/AIO-8550JD4_2_940d9755-4b93-4146-bea2-9e9584871c54.png?v=1788244299","url":"https:\/\/ai-paipai.store\/products\/aio-8550jd4-qcs8550-ai-motherboard","provider":"Ai-Paipai","version":"1.0","type":"link"}