{"product_id":"intel-arc-pro-b60-24gb","title":"Intel Arc Pro B60 (24GB)","description":"\u003cp\u003eThe Intel Arc Pro B60 steps up from the B50 to 24GB of memory on the same Xe2 “Battlemage” architecture. It is aimed at AI inference, generative design and 3D workloads where memory capacity matters as much as raw compute. Intel does not manufacture a reference-design B60: every card ships from a board partner (ASRock, Sparkle and others) built to Intel’s reference specification, so this listing is a partner card faithful to that spec rather than an Intel-branded reference board.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRunning quantized LLMs in the 13B–34B range locally\u003c\/li\u003e\n\u003cli\u003eLarger diffusion and vision models that would not fit in a 12GB or 16GB card\u003c\/li\u003e\n\u003cli\u003eProfessional visualization, CAD and generative design with dense 3D scenes and high-resolution textures\u003c\/li\u003e\n\u003cli\u003eTeams that want an affordable path to 48GB of pooled memory by pairing two cards\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e24GB GDDR6 on a 192-bit bus:\u003c\/strong\u003e up to 456GB\/s of bandwidth, well ahead of most cards in its price class, enough to hold large quantized AI models or complex 3D scenes entirely in VRAM.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e20 Xe2 cores, 160 XMX engines:\u003c\/strong\u003e a step up from the B50’s core count, with 20 dedicated ray tracing units for professional visualization work.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e197 TOPS INT8 AI performance:\u003c\/strong\u003e dedicated matrix acceleration for the low-precision math that dominates modern LLM and vision-model inference.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e12.28 TFLOPS FP32 compute:\u003c\/strong\u003e solid general shader performance for CAD, rendering and design applications.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-GPU ready:\u003c\/strong\u003e pairs of B60 cards can combine (Intel’s “Battlemage” dual-GPU and Project Battlematrix configurations) to pool up to 48GB of addressable memory for larger models.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePCIe 5.0 x8 interface:\u003c\/strong\u003e modern interface bandwidth with efficient, scalable power delivery.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e120-200W total board power:\u003c\/strong\u003e a wide, partner-dependent envelope that lets board vendors tune for efficiency or maximum clocks.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eSpec\u003c\/th\u003e\n\u003cth\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eIntel Xe2 “Battlemage,” TSMC N5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eXe2 cores\u003c\/td\u003e\n\u003ctd\u003e20 (160 XMX engines, 20 ray tracing units)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e24GB GDDR6, 192-bit bus\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003eUp to 456GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute performance\u003c\/td\u003e\n\u003ctd\u003e12.28 TFLOPS FP32 \/ up to 197 TOPS INT8\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal board power\u003c\/td\u003e\n\u003ctd\u003e120-200W (partner-dependent)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x8\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-GPU\u003c\/td\u003e\n\u003ctd\u003ePairable for up to 48GB pooled memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAvailability\u003c\/td\u003e\n\u003ctd\u003eBoard-partner card built to Intel reference specification (no Intel-branded reference card exists)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eGood to know\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBecause every B60 ships from a board partner, exact clocks, cooling and connector layout vary slightly by vendor. All cards adhere to Intel’s core specification above.\u003c\/li\u003e\n\u003cli\u003eIntel’s software ecosystem is newer than CUDA’s. oneAPI, OpenVINO and the driver stack support mainstream AI and CAD frameworks, but bleeding-edge research code that assumes NVIDIA hardware will need more setup work.\u003c\/li\u003e\n\u003cli\u003eStandard, well-supported tools such as llama.cpp, PyTorch with Intel extensions, OpenVINO-based inference and mainstream CAD applications are the comfortable fit.\u003c\/li\u003e\n\u003cli\u003eIntel has been expanding ISV certifications and driver validation for the Arc Pro line.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the B60 over the B50 when 24GB of memory is the deciding factor for your models or scenes. Choose a CUDA card instead if your code assumes NVIDIA hardware.\u003c\/p\u003e","brand":"Intel","offers":[{"title":"Default Title","offer_id":58232538038654,"sku":"INTEL-ARCB60","price":599.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-arc-b60_90788c39-e1f4-4f95-92ab-78d16424b6c8.png?v=1792094041","url":"https:\/\/computahardware.net\/products\/intel-arc-pro-b60-24gb","provider":"ComputaHardware","version":"1.0","type":"link"}