{"product_id":"axelera-embedded-113m","title":"Axelera Embedded 113m","description":"\u003cp class=\"MsoNormal\"\u003e\u003cspan style=\"mso-ansi-language: EN-US;\" lang=\"EN-US\"\u003e\u003cmeta charset=\"utf-8\"\u003e\n\u003cspan\u003eAxelera Embedded 113m \u003c\/span\u003epowered by \u003cstrong\u003eMetis AIPU,\u003c\/strong\u003e unlocks LLM and VLM applications on constrained edge and embedded devices.\u003c\/span\u003e\u003cstrong\u003e\u003cspan style=\"mso-ansi-language: EN-US;\" lang=\"EN-US\"\u003e \u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cb\u003e\u003cspan style=\"mso-ansi-language: EN-US;\" lang=\"EN-US\"\u003eKey Benefits:\u003c\/span\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli class=\"p2\"\u003eAxelera Embedded 113m  featuring Metis AIPU unlocks high performance for LLMs and VLMs by maximizing memory performance on small footprint devices and with power constraints.\u003c\/li\u003e\n\u003cli class=\"p2\"\u003eThe card can run computer vision inference tasks on multiple cameras using multiple cascaded or parallel models.\u003c\/li\u003e\n\u003cli class=\"p2\"\u003eAxelera Embedded 113m is available in memory sizes up to 8 GB* and is offered standalone or with a cooling solution providing, flexibility across a range of application needs.\u003c\/li\u003e\n\u003cli class=\"p2\"\u003eIt is available in standard operating temperature range (-20°C to +70°C) as well as extended operating temperature range (-40°C to +85°C).\u003c\/li\u003e\n\u003cli class=\"p2\"\u003eFirmware integrity protection is provided through secure boot and secure upgrade features built on a hardware Root-of-Trust in the Metis AIPU.\u003c\/li\u003e\n\u003cli class=\"p2\"\u003eA wide range of end-to-end AI pipelines and models are available out of the box.\u003c\/li\u003e\n\u003cli class=\"p2\"\u003eHassle free evaluation and software integration thanks to Voyager® SDK.\u003c\/li\u003e\n\u003cli class=\"p2\"\u003eUncompromised prediction accuracy thanks to advanced quantization tools.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"MsoNormal\"\u003e*Axelera Embedded 113m card is also available in \u003cspan\u003eadditional\u003c\/span\u003e memory configurations for volume orders. Contact us to discuss specifications.\u003c\/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e\u003cspan style=\"mso-ansi-language: EN-GB;\" lang=\"EN-GB\"\u003eSystem Requirements\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eAxelera Embedded 113m Card has the following system integration requirements. Our team actively tests integration with different systems from vendors such as Dell, Lenovo, Advantech and Aetina.\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eHost Interface: PCIe Gen. 3.0 x4\u003c\/li\u003e\n\u003cli\u003eMechanical form factor: M.2 2280 M-key requires H5.6.\u003c\/li\u003e\n\u003cli\u003ePower Rating: Compliant with PCI-SIG’s M.2 Specification revision 4.0 (11.55 W average power, 23.1 W peak power).\u003c\/li\u003e\n\u003cli\u003eHost CPU: Intel® Core™ Processors and Xeon(R) Processors, Ryzen™ Processors, Arm64 (aarch64) based processors.\u003c\/li\u003e\n\u003cli\u003eHost Operating System:\n\u003cul\u003e\n\u003cli\u003eNative Linux support is tested on Ubuntu 22.04 and Ubuntu 24.04\u003c\/li\u003e\n\u003cli\u003e\n\u003cmeta charset=\"utf-8\"\u003e \u003cspan\u003eA \u003c\/span\u003e\u003ca rel=\"noopener noreferrer\" href=\"https:\/\/eur05.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fsupport.axelera.ai%2Fhc%2Fen-us%2Farticles%2F25953148201362-Install-Voyager-SDK-in-a-Docker-Container\u0026amp;data=05%7C02%7Cwendy.vargas%40axelera.ai%7Cb9c1046c3c9c4d7cef8808dd9246df22%7C9c838ba7c38c416da7931de07a190ebd%7C0%7C0%7C638827555423750785%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C\u0026amp;sdata=VWiCrReAv22sAa54inxg1OU92ErdEQMWO5PincTgvTA%3D\u0026amp;reserved=0\" title=\"Original URL: https:\/\/support.axelera.ai\/hc\/en-us\/articles\/25953148201362-Install-Voyager-SDK-in-a-Docker-Container. Click or tap if you trust this link.\" target=\"_blank\"\u003eDocker Guide\u003c\/a\u003e\u003cspan\u003e is available to evaluate or develop with other Linux distributions.\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eNative Windows support for inference is available on Windows 10\/11 and Windows Server 2025. SDK development is only supported on Linux.\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca class=\"button\" href=\"https:\/\/axelera.ai\/hubfs\/Axelera%20AI%20M.2%20Max%20AI%20Edge%20accelerator%20Card.pdf?hsLang=en\" rel=\"noopener\" target=\"_blank\"\u003e\u003cspan lang=\"EN-GB\" style=\"mso-ansi-language: EN-GB;\"\u003eDownload the M.2 MAX Product Brief here!\u003c\/span\u003e\u003c\/a\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cstyle\u003e\n.button {\n   background-color: #f4be18;\n   color: #000000;\n   font-size: 16px;\n   padding: 10px 30px;\n   text-align: center;\n   display: inline-block;\n   text-decoration: none; \/* Removes underline *\/\n   border-radius: 22px;\n   transition: background 0.4s ease; \/* Smooth transition *\/\n}\n.button:hover {\n   background: linear-gradient(90deg, #f4be18 50%, #5aa67a 65%, #3e53e5 80%, #3e53e5 90%);\n   color: #fff;\n   text-decoration: none;\n}\n\u003c\/style\u003e\n\u003cp class=\"MsoListParagraphCxSpFirst\" style=\"text-indent: -18.0pt; mso-list: l1 level1 lfo2;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan lang=\"EN-GB\" style=\"font-family: Symbol; mso-fareast-font-family: Symbol; mso-bidi-font-family: Symbol; mso-ansi-language: EN-GB;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003eD·\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003cspan lang=\"EN-GB\" style=\"mso-ansi-language: EN-GB;\"\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Axelera AI","offers":[{"title":"Default Title","offer_id":51397171315029,"sku":"AXE-BME21M1BO08A04","price":379.95,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0879\/1664\/2645\/files\/Axelera-AI-Metis-M.2-Max-Accelerator-Card-Rev2-No-Cooling-Front-View.webp?v=1780314163","url":"https:\/\/store.axelera.ai\/products\/axelera-embedded-113m","provider":"Axelera AI","version":"1.0","type":"link"}