{"title":"AMD","description":null,"products":[{"product_id":"amd-radeon-rx-7900-xtx-24gb","title":"AMD Radeon RX 7900 XTX (24GB)","description":"\u003cp\u003eThe AMD Radeon RX 7900 XTX is AMD’s flagship RDNA 3 gaming card and the first consumer GPU to use a chiplet design, giving it a 24GB memory pool and near-flagship performance at a price well below the equivalent NVIDIA tier. It suits gamers chasing high-refresh 4K, streamers who need encode headroom and Linux users who want a fully open driver stack.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eHigh-refresh native 4K gaming where rasterization performance and VRAM matter most\u003c\/li\u003e\n\u003cli\u003eStreamers and content creators using AMD’s dual encode engines and OpenCL\/HIP compute in supported applications\u003c\/li\u003e\n\u003cli\u003eLocal image generation and smaller LLM inference, which a 12GB or 16GB card cannot do as comfortably\u003c\/li\u003e\n\u003cli\u003eLinux users who want ROCm and open-source PyTorch-based AI\/ML development\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eChiplet-based Navi 31 GPU:\u003c\/strong\u003e a 5nm graphics compute die paired with six 6nm memory cache dies (MCDs), the first gaming GPU to split compute and memory logic across separate chiplets for better yields and cost efficiency.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96 Compute Units, 6,144 Stream Processors:\u003c\/strong\u003e high-throughput parallel compute for rendering, ray tracing and general GPU workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e24GB GDDR6 on a 384-bit bus:\u003c\/strong\u003e more memory than NVIDIA’s equivalent-tier competition, at up to 20Gbps for 960GB\/s of raw bandwidth.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96MB AMD Infinity Cache:\u003c\/strong\u003e a large on-die cache that lifts effective memory bandwidth up to 3,500GB\/s, keeping the GPU fed at 4K and beyond without a wider memory bus.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96 second-generation Ray Accelerators and 192 AI Accelerators:\u003c\/strong\u003e dedicated hardware for ray tracing and AI-assisted upscaling (FSR) workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUp to 2,500MHz boost clock:\u003c\/strong\u003e 2,300MHz game clock, for consistent frame rates under load.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e61.4 TFLOPS FP32:\u003c\/strong\u003e strong raw compute for gaming and GPU-accelerated creative applications.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eROCm support on Linux:\u003c\/strong\u003e an open-source path into PyTorch-based AI\/ML development, without proprietary CUDA tooling.\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\u003eAMD RDNA 3 (Navi 31), chiplet design: 5nm GCD + 6nm MCDs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute units\u003c\/td\u003e\n\u003ctd\u003e96 (6,144 Stream Processors)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAccelerators\u003c\/td\u003e\n\u003ctd\u003e96 Ray Accelerators (2nd-generation) \/ 192 AI Accelerators\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e24GB GDDR6, 384-bit bus, 20Gbps\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e960GB\/s (up to 3,500GB\/s effective with Infinity Cache)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInfinity Cache\u003c\/td\u003e\n\u003ctd\u003e96MB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP32 compute\u003c\/td\u003e\n\u003ctd\u003e61.4 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eClocks\u003c\/td\u003e\n\u003ctd\u003e2,300MHz game \/ up to 2,500MHz boost\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal board power\u003c\/td\u003e\n\u003ctd\u003e355W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower connectors\u003c\/td\u003e\n\u003ctd\u003e2x 8-pin PCIe (reference design)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay outputs\u003c\/td\u003e\n\u003ctd\u003eDisplayPort 2.1, HDMI 2.1, USB-C (reference design)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLaunch\u003c\/td\u003e\n\u003ctd\u003eDecember 2022, original MSRP $999\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\u003eAt native 4K it trades blows with NVIDIA’s previous-generation flagship class while typically costing less. The trade is that DLSS and ray tracing quality still generally lead FSR and AMD’s ray accelerators in the most demanding path-traced titles.\u003c\/li\u003e\n\u003cli\u003eROCm is open and improving, but it does not yet have the plug-and-play maturity of CUDA for AI\/ML work, particularly on Windows. Support is strongest on Linux, where ROCm is officially validated for this GPU.\u003c\/li\u003e\n\u003cli\u003e24GB of VRAM holds up better than 16GB competitors as texture and asset sizes grow, and titles that felt VRAM-constrained on an 8GB or 12GB card will not be an issue. Games running at 1440p today have room to move up to native 4K.\u003c\/li\u003e\n\u003cli\u003eThe Adrenalin driver suite offers strong feature parity for streaming, recording and performance tuning.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the RX 7900 XTX for the most VRAM and raw performance per pound in AMD’s lineup, if you are comfortable with FSR instead of DLSS. If best-in-class ray tracing or CUDA matters more, look at NVIDIA.\u003c\/p\u003e","brand":"AMD","offers":[{"title":"Default Title","offer_id":58232537612670,"sku":"AMD-RX7900XTX","price":899.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-rx-7900xtx.jpg?v=1792094035"},{"product_id":"amd-radeon-pro-w7900-48gb","title":"AMD Radeon PRO W7900 (48GB)","description":"\u003cp\u003eThe AMD Radeon PRO W7900 is AMD’s flagship workstation graphics card. Its defining feature is 48GB of ECC-protected memory on the full RDNA 3 Navi 31 die, in a dual-slot form factor that fits real workstation chassis. It suits studios and engineering teams running large AI models, professional rendering pipelines or scientific visualization workloads that outgrow a 24GB consumer card.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRunning 30B-parameter class LLMs and keeping several models resident for a production inference pipeline\u003c\/li\u003e\n\u003cli\u003eFine-tuning mid-size open-weight models without chunking datasets or offloading to system memory\u003c\/li\u003e\n\u003cli\u003eArchitectural and product visualization, full-resolution VFX shots and simulation caches held entirely in GPU memory\u003c\/li\u003e\n\u003cli\u003eEngineering firms standardizing on one ECC-protected, ISV-certified card per seat\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e48GB ECC GDDR6:\u003c\/strong\u003e the full Navi 31 memory configuration on a 384-bit bus, with error-correcting protection for professional reliability, for LLMs, production scenes and massive point-cloud, 3D scan or CAD datasets on-card.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96 Compute Units, 6,144 Stream Processors:\u003c\/strong\u003e the full Navi 31 die, set up for workstation workloads rather than gaming clocks.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e96 second-generation Ray Accelerators, 192 AI Accelerators:\u003c\/strong\u003e dedicated hardware for professional ray-traced rendering and AI-assisted workflows.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e864GB\/s memory bandwidth:\u003c\/strong\u003e keeps the compute units fed when working with the large datasets this memory capacity allows.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e61 TFLOPS FP32 \/ 122 TFLOPS FP16:\u003c\/strong\u003e strong compute for rendering, simulation and mixed-precision AI workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDual-slot workstation design:\u003c\/strong\u003e a more space-efficient footprint than earlier triple-slot professional cards, so integrators can fit up to four W7900s in one multi-GPU workstation.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eROCm certified:\u003c\/strong\u003e validated for professional AI and ML development, with ISV-certified drivers for major CAD, DCC and simulation applications.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQuad DisplayPort 2.1 outputs:\u003c\/strong\u003e three full-size DisplayPort 2.1 plus one mini-DisplayPort 2.1, each capable of 54Gbps, for high-resolution multi-monitor CAD and visualization.\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\u003eAMD RDNA 3 (full Navi 31 die)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute units\u003c\/td\u003e\n\u003ctd\u003e96 (6,144 Stream Processors, 192 AI Accelerators, 96 Ray Accelerators)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e48GB ECC GDDR6, 384-bit bus\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e864GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute performance\u003c\/td\u003e\n\u003ctd\u003e61 TFLOPS FP32 \/ 122 TFLOPS FP16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eClocks\u003c\/td\u003e\n\u003ctd\u003e1,855MHz core \/ 2,495MHz boost\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal board power\u003c\/td\u003e\n\u003ctd\u003e295W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower connectors\u003c\/td\u003e\n\u003ctd\u003e2x 8-pin PCIe\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm factor\u003c\/td\u003e\n\u003ctd\u003eDual-slot workstation card, 280mm length\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay outputs\u003c\/td\u003e\n\u003ctd\u003e3x DisplayPort 2.1 + 1x mini-DisplayPort 2.1\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSoftware\u003c\/td\u003e\n\u003ctd\u003eROCm certified, ISV-certified professional drivers\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\u003eROCm is officially supported and certified for this card, but the CUDA ecosystem still has a head start in framework coverage, especially for bleeding-edge AI research code that assumes NVIDIA hardware.\u003c\/li\u003e\n\u003cli\u003eTeams already standardized on PyTorch and mainstream inference frameworks have a well-supported path on ROCm.\u003c\/li\u003e\n\u003cli\u003eOne W7900 replaces what would otherwise take two or more 24GB cards, without the extra system complexity, driver overhead or PCIe lane contention of a multi-GPU build.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the W7900 when memory headroom is the problem and your workflows are rendering-, CAD- or mainstream-AI-first. It is a low-risk, high-capacity upgrade; if your code needs CUDA, look elsewhere.\u003c\/p\u003e","brand":"AMD","offers":[{"title":"Default Title","offer_id":58232537940350,"sku":"AMD-W7900","price":3999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-w7900.jpg?v=1792094037"},{"product_id":"amd-radeon-ai-pro-r9700-32gb","title":"AMD Radeon AI PRO R9700 (32GB)","description":"\u003cp\u003eThe AMD Radeon AI PRO R9700 is AMD’s first RDNA 4 workstation card. It gives you 32GB of memory in a 2-slot blower design, aimed at teams who want serious local AI capability without stepping up to a 48GB flagship card or a datacenter GPU. It suits AI developers, data scientists and technical creators who need local inference and fine-tuning headroom on a workstation budget.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRunning quantized LLMs in the 30B+ parameter range locally\u003c\/li\u003e\n\u003cli\u003eFine-tuning smaller models entirely on the card\u003c\/li\u003e\n\u003cli\u003eBuilding and testing RAG pipelines before deploying to cloud infrastructure\u003c\/li\u003e\n\u003cli\u003eMulti-GPU AI workstations that pool memory across several cards in one chassis\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRDNA 4 architecture (Navi 48):\u003c\/strong\u003e AMD’s newest GPU architecture on TSMC’s 4nm process, a generational leap in AI-specific hardware over the previous RDNA 3 workstation cards.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e32GB GDDR6 on a 256-bit bus:\u003c\/strong\u003e 20Gbps for 640GB\/s of bandwidth, enough for quantized LLMs in the 30B+ parameter range or fine-tuning smaller models on-card.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e64 Compute Units, 4,096 Stream Processors:\u003c\/strong\u003e plus 64 second-generation Ray Accelerators for professional rendering and visualization work.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e128 second-generation AI Accelerators:\u003c\/strong\u003e up to 95.7 TFLOPS of dense FP16 compute and up to 1,531 TOPS of INT4 performance with sparsity, tuned for the low-precision matrix math (INT4, INT8, FP8) of quantized inference.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-GPU scaling:\u003c\/strong\u003e pool memory and compute across multiple R9700 cards for larger models than one card can hold, at a lower cost per gigabyte than a flagship card, provided your framework and workload support multi-GPU sharding.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e2-slot blower cooling:\u003c\/strong\u003e a compact, workstation-friendly form factor that supports dense multi-GPU builds.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQuad DisplayPort 2.1a outputs:\u003c\/strong\u003e full multi-monitor support for engineering and creative work alongside AI workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eROCm on Linux and Windows:\u003c\/strong\u003e an open software stack for AI development, officially supported on both major desktop operating systems.\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\u003eAMD RDNA 4 (Navi 48), TSMC 4nm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute units\u003c\/td\u003e\n\u003ctd\u003e64 (4,096 Stream Processors)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAccelerators\u003c\/td\u003e\n\u003ctd\u003e64 Ray Accelerators (2nd-gen) \/ 128 AI Accelerators (2nd-gen)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e32GB GDDR6, 256-bit bus, 20Gbps\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e640GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute performance\u003c\/td\u003e\n\u003ctd\u003e47.8 TFLOPS FP32 \/ 95.7 TFLOPS FP16 \/ up to 1,531 TOPS INT4 (sparse)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eClocks\u003c\/td\u003e\n\u003ctd\u003e1,620MHz base \/ 2,920MHz boost\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal board power\u003c\/td\u003e\n\u003ctd\u003e300W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower connector\u003c\/td\u003e\n\u003ctd\u003e1x 12V-2×6\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm factor\u003c\/td\u003e\n\u003ctd\u003e2-slot blower\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay outputs\u003c\/td\u003e\n\u003ctd\u003e4x DisplayPort 2.1a\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLaunch\u003c\/td\u003e\n\u003ctd\u003eJuly 2025, MSRP $1,299\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\u003eROCm is officially supported on this card on Linux and Windows, but framework compatibility and out-of-the-box performance still generally trail CUDA on the newest, most experimental AI research code.\u003c\/li\u003e\n\u003cli\u003eMainstream inference stacks such as llama.cpp, vLLM and PyTorch with ROCm backends are the better fit than bleeding-edge research repos.\u003c\/li\u003e\n\u003cli\u003eThe card has more memory and AI-specific hardware than any consumer gaming GPU at a similar price, and it also suits GPU-accelerated rendering and data-heavy visualization.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the R9700 as a first card for local inference infrastructure, with room to add cards once your workload and frameworks are proven. If you need 48GB on a single card, look at the tier above.\u003c\/p\u003e","brand":"AMD","offers":[{"title":"Default Title","offer_id":58232537973118,"sku":"AMD-R9700","price":1299.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/gpu-r9700_629f0734-5b66-4828-a058-c8c40bfa8615.png?v=1792094038"},{"product_id":"amd-ryzen-ai-halo-developer-platform-ryzen-ai-max-395","title":"AMD Ryzen AI Halo Developer Platform (Ryzen AI Max+ 395)","description":"\u003cp\u003eThe AMD Ryzen AI Halo Developer Platform is AMD's own reference-hardware dev kit built around the Ryzen AI Max+ 395 Strix Halo processor. It runs local LLM inference and fine-tuning on x86 hardware with full ROCm support. It suits ML engineers and researchers whose toolchain, CI pipeline or model zoo assumes x86_64 and who want an AMD-native alternative to the NVIDIA GB10 systems.\u003c\/p\u003e\u003ch2\u003eWho it's for\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eRunning open-weight models in the 30B–70B parameter range locally\u003c\/li\u003e\n\u003cli\u003eFine-tuning and inference on frameworks with solid ROCm support, such as PyTorch, llama.cpp and vLLM's ROCm backend\u003c\/li\u003e\n\u003cli\u003eTeams that depend on x86 Docker images and Python wheels\u003c\/li\u003e\n\u003cli\u003eDevelopers evaluating ROCm with ready-made setups for LM Studio, ComfyUI and VS Code\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eKey features\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAMD Ryzen AI Max+ 395 processor:\u003c\/strong\u003e 16 Zen 5 CPU cores \/ 32 threads at up to 5.1GHz boost (3.0GHz base), with 64MB of L3 cache, built on a 4nm process.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIntegrated Radeon 8060S GPU:\u003c\/strong\u003e 40 RDNA 3.5 compute units running up to 2.9GHz, for graphics and compute-shader throughput alongside the AI silicon.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUp to 126 TOPS combined AI performance:\u003c\/strong\u003e The XDNA 2 NPU delivers 50 TOPS on its own; 126 TOPS is the combined CPU + GPU + NPU total AMD publishes.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e128GB unified LPDDR5x-8000 at 256GB\/s:\u003c\/strong\u003e One memory pool shared across CPU, GPU and NPU, with AMD Variable Graphics Memory letting you allocate most of it to the GPU as VRAM.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e2TB NVMe SSD pre-installed:\u003c\/strong\u003e Local storage for model weights, datasets and containers.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFull ROCm support out of the box:\u003c\/strong\u003e AMD's ROCm stack comes pre-configured, with AMD AI Playbooks and ready-to-run setups for LM Studio, ComfyUI and VS Code.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e10GbE, Wi-Fi 7 and Bluetooth 5.4:\u003c\/strong\u003e Wired and wireless networking, plus 3x USB-C ports (with DisplayPort Alt Mode) and HDMI 2.1b.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCompact chassis:\u003c\/strong\u003e 150 x 150 x 45.4mm, roughly 1.2kg, with a 120W TDP.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eSpecifications\u003c\/h2\u003e\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\u003eProcessor\u003c\/td\u003e\n\u003ctd\u003eAMD Ryzen AI Max+ 395, 16 cores \/ 32 threads (Zen 5), 3.0GHz base \/ up to 5.1GHz boost, 64MB L3 cache\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProcess node\u003c\/td\u003e\n\u003ctd\u003eTSMC 4nm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU\u003c\/td\u003e\n\u003ctd\u003eAMD Radeon 8060S, 40 RDNA 3.5 compute units, up to 2.9GHz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNPU\u003c\/td\u003e\n\u003ctd\u003eAMD XDNA 2, up to 50 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCombined AI performance\u003c\/td\u003e\n\u003ctd\u003eUp to 126 TOPS (CPU + GPU + NPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e128GB unified LPDDR5x-8000 (256-bit bus)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e256GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStorage\u003c\/td\u003e\n\u003ctd\u003e2TB M.2 NVMe SSD, pre-installed\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNetworking\u003c\/td\u003e\n\u003ctd\u003e10 Gigabit Ethernet, Wi-Fi 7, Bluetooth 5.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePorts\u003c\/td\u003e\n\u003ctd\u003e3x USB-C (DisplayPort Alt Mode support), HDMI 2.1b\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSoftware\u003c\/td\u003e\n\u003ctd\u003eFull AMD ROCm support, AMD AI Playbooks, LM Studio \/ ComfyUI \/ VS Code preconfigured\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating system\u003c\/td\u003e\n\u003ctd\u003eWindows 11 Pro or Linux (custom AMD BIOS)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTDP\u003c\/td\u003e\n\u003ctd\u003e120W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDimensions\u003c\/td\u003e\n\u003ctd\u003e150 x 150 x 45.4mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight\u003c\/td\u003e\n\u003ctd\u003eApprox. 1.2kg\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConfigurations\u003c\/td\u003e\n\u003ctd\u003eWindows 11 Pro, Linux OS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\u003ch2\u003eGood to know\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eROCm has matured substantially, but it does not yet match CUDA's pre-built container images, third-party tutorials and community troubleshooting history. For teams invested in CUDA-specific tooling, that is a real migration cost.\u003c\/li\u003e\n\u003cli\u003eIt ships as AMD reference hardware with a custom AMD BIOS, and you choose either Windows 11 Pro or a Linux configuration.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eChoose the Ryzen AI Halo Developer Platform if you need x86 compatibility and a well-supported ROCm setup for local AI development. If your workflow is CUDA-first, an NVIDIA GB10 system is the closer match.\u003c\/p\u003e","brand":"AMD","offers":[{"title":"Windows 11 Pro","offer_id":58232540103038,"sku":"AMD-RAH-001-WINOS","price":4899.99,"currency_code":"USD","in_stock":true},{"title":"Linux OS","offer_id":58232540135806,"sku":"AMD-RAH-001-LINUXOS","price":3999.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/new-amd-ryzen-ai-halo-1_71ce8a46-6de7-48af-9190-f28b0e4a63bd.jpg?v=1792094098"}],"url":"https:\/\/computahardware.net\/collections\/amd.oembed","provider":"ComputaHardware","version":"1.0","type":"link"}