{"product_id":"radxa-aicore-ax-m1-2","title":"RADXA AICore AX-M1","description":"\u003cp\u003eRadxa AICore AX‑M1 is a high‑performance M.2 acceleration module based on Axera’s\u003cbr\u003eAX8850 SoC, featuring high computing power and energy efficiency ratio, specifically\u003cbr\u003edesigned for edge AI computing and AI inference applications.\u003c\/p\u003e\n\u003cp\u003eIt integrates a multi‑core high‑frequency CPU with a dedicated neural processing unit (NPU), delivering exceptional multimedia performance and efficient hardware acceleration for diverse edge AI applications\u003c\/p\u003e\n\u003cp\u003e\u003c!-- split --\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003c\/span\u003e\u003cspan\u003e\u003cstrong\u003eRADXA AICore AX-M1 product details\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAX8850 SoC\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eOcta‑core Cortex‑A55 processor with a frequency up to 1.5GHz\u003c\/p\u003e\n\u003cp style=\"padding-left: 30px;\"\u003e\u003cspan\u003e\u003cspan class=\"fontstyle0\"\u003eNPU – 24TOPS@INT8, Supports Matrix Arithmetic Unit (MAU) and Intelligent\u003cbr\u003eVideo Engine (IVE)\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp style=\"padding-left: 30px;\"\u003e\u003cspan\u003e\u003cspan class=\"fontstyle0\"\u003eVPU – Supports H.264\/H.265 8K@30fps codec and 16‑channel 1080p@30fps\u003cbr\u003edecoding\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cspan class=\"fontstyle0\"\u003e\u003cstrong\u003eAI Performance\u003c\/strong\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e25 TOPS\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cspan class=\"fontstyle0\"\u003e\u003cstrong\u003eMemory\u003c\/strong\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e8GB LPDDR4x\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eStorage\u003c\/strong\u003e\u003cstrong\u003e \u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cspan class=\"fontstyle0\"\u003eQSPI 1Gbit NAND \/ NOR Flash\u003cbr\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong class=\"fontstyle0\"\u003eHost Interface\u003c\/strong\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003eM.2 M Key\u003c\/p\u003e\n\u003cp\u003e\u003cstrong class=\"fontstyle0\"\u003eHost System\u003c\/strong\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003eSupports mainstream host platforms including Intel, AMD, Rockchip,\u003cbr\u003eetc.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"fontstyle0\"\u003e・ Support for Ubuntu, Debian, CentOS, etc.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Frameworks\u003c\/strong\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 0.875rem;\"\u003e\u003cspan class=\"fontstyle0\"\u003e・ Support TensorFlow\u003cbr\u003e・ ONNX\u003cbr\u003e・ Keras\u003cbr\u003e・ PyTorch by DX‑COM compiler converted\u003cbr\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperating Temperature\u003c\/strong\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e‑25 ~ 65°C (Non‑Throttling) \/ ‑25 ~ 85°C (Throttling)\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eHost Platform\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eSupports mainstream host platforms including Intel, AMD, Rockchip,\u003cbr\u003eetc.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower Consumption\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"fontstyle0\"\u003e@3.3V ≤ 8W\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e\u003cu\u003e\u003c\/u\u003e\u003c\/strong\u003e\u003c\/span\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 0.875rem;\"\u003eM.2 M Key, 2280 (22mm x 80mm)\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cimg\u003e\u003cimg height=\"65\" width=\"295\" alt=\"\" src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0021\/1497\/7894\/files\/Rohrs_CE_1.png?v=1779939667\"\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e______________________________________________________\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan\u003eAdditional information \u0026amp; Setup instructions\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eunder preparation\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e______________________________________________________\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eAvailable until at least July 2030\u003c\/span\u003e\u003c\/p\u003e","brand":"RADXA","offers":[{"title":"Default Title","offer_id":53091397402940,"sku":"RM650","price":199.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0021\/1497\/7894\/files\/Angle1_b896ce62-12a6-452f-864f-a3dcf9db690d.png?v=1779950290","url":"https:\/\/shop.allnetchina.cn\/products\/radxa-aicore-ax-m1-2","provider":"ALLNET China","version":"1.0","type":"link"}