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Not sure if you're on the right track? Let our experts review your work before you burn more budget.",[283,284,285],"Smart contract review","Architecture and roadmap analysis","Actionable expert recommendations","€2,000","Pay €1,000 with voucher",{"title":241,"titleMaxWidth":242,"items":289},[290,292,294,297],{"number":245,"color":246,"title":247,"subtitle":248,"bullets":291,"voucherPercent":253,"voucherValue":254,"voucherNote":255},[250,251,252],{"number":257,"color":258,"title":259,"subtitle":260,"bullets":293,"voucherPercent":264,"voucherValue":265,"voucherNote":266},[262,263],{"number":268,"color":269,"title":270,"subtitle":271,"bullets":295,"voucherPercent":275,"voucherValue":296,"voucherNote":266},[273,274],"€25,000",{"number":278,"color":279,"title":280,"subtitle":281,"bullets":298,"voucherPercent":264,"voucherValue":286,"voucherNote":287},[283,284,285],"ljk891863m9r9y0","packages","2025-05-20 08:14:43.436Z",{"category":303,"chapterLinks":17,"clientName":17,"collectionId":304,"collectionName":305,"content":306,"created":307,"embedAlt":17,"embedPlaceholder":308,"embedUrl":17,"id":309,"lang":310,"order":311,"resultsContent":17,"reviewAuthor":17,"reviewContent":17,"reviewTitle":17,"shortDescription":312,"slug":313,"subtitle":17,"tags":314,"tech":17,"techDescription":17,"techTitle":17,"title":315,"updated":316},[60],"pbc_999467826","case_studies","\u003Cp dir=\"ltr\">Following the debut of the NVIDIA RTX Spark \"Superchip,\" we conducted a comparative analysis to evaluate its potential impact against our existing AMD Ryzen AI Max+ workflows. This research examines what NVIDIA’s upcoming unified architecture will deliver for local inference.\u003Cbr>\u003Cbr>Large language models (LLMs) are no longer tested only in the cloud. For development teams working on AI systems, the ability to shift workloads directly to edge devices is becoming increasingly important to address corporate demands for data sovereignty, latency reduction, and the elimination of recurring cloud API fees.\u003C\u002Fp>\r\n\u003Cp dir=\"ltr\">However, executing modern frontier LLMs requires hardware capable of bypassing the traditional memory bottlenecks found in standard consumer systems. Below is a practical overview comparing two unified memory architecture (UMA) platforms: the x86-based AMD Ryzen AI Max+ 395 \"Strix Halo\" APU and the ARM-based NVIDIA RTX Spark \"Superchip\".\u003C\u002Fp>\r\n\u003Cp dir=\"ltr\">[cta text=\"Free consulting\" to=\"\u002Fdigitalization\u002Fai-svetovanje\" color=\"yellow\"]\u003Cstrong id=\"docs-internal-guid-6ddde856-7fff-e737-ee49-b1aa9cad1a43\">\u003C\u002Fstrong>\u003C\u002Fp>\r\n\u003Ch2 dir=\"ltr\">The hardware setups: AMD vs. NVIDIA\u003C\u002Fh2>\r\n\u003Cp dir=\"ltr\">The AMD Ryzen AI Max+ 395 and the NVIDIA RTX Spark represent two distinct design philosophies for unified computing.\u003C\u002Fp>\r\n\u003Cp dir=\"ltr\">AMD Ryzen AI Max+ 395 AMD’s setup focuses on a multi-chiplet design. In practice, this platform:\u003C\u002Fp>\r\n\u003Cul>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Is built on TSMC's 4 nm FinFET process.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Features two CPU Core Complex Dies housing 16 high-performance \"Zen 5\" cores with 32 threads.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Integrates a Radeon 8060S graphics engine, memory controllers, and an AMD XDNA 2 NPU (capable of 50 TOPS) on a central I\u002FO die.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Relies on high-speed internal buses on its monolithic I\u002FO die to manage data.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Operates on a configurable TDP of 45 W to 120 W.\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Cp dir=\"ltr\">NVIDIA RTX Spark Co-developed with MediaTek, NVIDIA takes a dual-chiplet \"superchip\" approach. This platform:\u003C\u002Fp>\r\n\u003Cul>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Is built on TSMC's 3 nm EUV process.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Couples a custom 20-core ARM \"Grace\" CPU with a client \"Blackwell\" RTX graphics processor.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Features 48 Streaming Multiprocessors with 6,144 CUDA cores.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Utilizes an on-package NVLink-C2C interconnect, providing up to 600 GB\u002Fs of coherent, low-latency bandwidth.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Uses custom power management to dynamically scale up to 80 W, offering high power efficiency on battery.\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Ch2 dir=\"ltr\"> \u003C\u002Fh2>\r\n\u003Ch2 dir=\"ltr\">Memory management: VRAM and bandwidth\u003C\u002Fh2>\r\n\u003Cp dir=\"ltr\">Local LLM execution is primarily constrained by memory bandwidth rather than raw compute operations. Both platforms offer wider memory buses than standard x86 laptop platforms, which are typically restricted to a 128-bit interface.\u003C\u002Fp>\r\n\u003Cp dir=\"ltr\">Memory behavior on the AMD Ryzen AI Max+ 395:\u003C\u002Fp>\r\n\u003Cul>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Features Quad-Channel LPDDR5X-8000 memory.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Offers a unified memory bandwidth of 256 GB\u002Fs.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Has default BIOS configurations that restrict the physical memory reserved as VRAM, capping maximum selectable limits at 96 GB.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Allows developers using Linux to adjust Translation Table Manager (TTM) kernel parameters to bypass BIOS constraints, allocating up to 120 GB of a 128 GB system pool as usable VRAM.\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Cp dir=\"ltr\">Memory behavior on the NVIDIA RTX Spark:\u003C\u002Fp>\r\n\u003Cul>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Features unified LPDDR5X memory with a capacity of up to 128 GB.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Delivers up to 300 GB\u002Fs of unified memory bandwidth.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Employs a fully dynamic allocation system without manual partition adjustments.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Allows the Blackwell GPU and Grace CPU to share the entire memory pool seamlessly, eliminating data copying overhead.\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Ch2 dir=\"ltr\">Scaling up: Multi-node vs. hardware link\u003C\u002Fh2>\r\n\u003Cp dir=\"ltr\">For workloads requiring memory pools larger than 128 GB, both architectures support scaling beyond a single integrated GPU with clear trade-offs.\u003C\u002Fp>\r\n\u003Cul>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Multi-Node Scaling on AMD: Developers can connect multiple physical nodes via high-speed Ethernet and use llama.cpp's RPC protocol to split model layers. This allows a four-APU cluster to run a 1-trillion-parameter model locally.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Hardware Link Scaling on NVIDIA: Two physical RTX Spark desktop units can be connected directly via an external interconnect. This creates a cohesive 256 GB shared-memory system without network overhead, enabling the local execution of models up to 200 billion parameters.\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Ch2 dir=\"ltr\">Software frameworks: Vulkan, ROCm, and CUDA\u003C\u002Fh2>\r\n\u003Cp dir=\"ltr\">The software environments of these two platforms dictate their ease of deployment, library support, and long-term stability.\u003C\u002Fp>\r\n\u003Cp dir=\"ltr\">The AMD stack: Vulkan vs. ROCm\u003C\u002Fp>\r\n\u003Cul>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Vulkan: This API is highly stable on client APUs and can deliver good generation speeds. \u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">ROCm: Because it was originally designed for enterprise server GPUs, it can cause driver and library conflicts on client-facing APUs. This was greatly improved with the new versions. For example, a Qwen coder 60B model yields 36 t\u002Fs under ROCm supported  Ollama.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">Hybrid execution: Using ONNX Runtime GenAI, the 50 TOPS NPU can handle the initial prefill phase silently on less than 15 W of power, leaving the GPU to process the high-bandwidth decode phase.\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Cp>\u003Cbr>Read more about running LLMs on AMD AI MAX - \u003Ca href=\"https:\u002F\u002Fkalmia.si\u002Fblog\u002Frunning-llms-on-amd-framework-vulkan-rocm\" target=\"_blank\" rel=\"noopener\">here.\u003C\u002Fa> \u003C\u002Fp>\r\n\u003Cp dir=\"ltr\">\u003Cbr>The NVIDIA stack: CUDA and OpenShell\u003C\u002Fp>\r\n\u003Cul>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">CUDA and TensorRT: Standard packages like PyTorch and vLLM run natively on the platform without requiring custom environment variables or version overrides.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">OpenShell: An open-source runtime that integrates with Windows security primitives to run autonomous agents in isolated, metered sandboxes. It automatically masks personal information in outgoing queries to protect user privacy.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">ARM Emulation: Because the CPU uses the ARM instruction set, running legacy x86 tools relies on Microsoft's Prism emulation layer. Compiling llama.cpp from source takes up to three times longer on the Grace ARM CPU than on a standard x86 Zen 5 CPU.\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Ch2 dir=\"ltr\">Practical state of inference and benchmarks\u003C\u002Fh2>\r\n\u003Cp dir=\"ltr\">When testing local inference speeds, raw computational performance matters less than quantization formats and memory architecture.\u003C\u002Fp>\r\n\u003Cul>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">The AMD platform proves highly efficient when running Mixture-of-Experts (MoE) models, which reduce memory bandwidth demands. For instance, a 120B parameter MoE model (GPT-OSS-120B) runs at a comfortable 47 t\u002Fs on this setup.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">The NVIDIA RTX Spark leverages its Blackwell Tensor Cores for native FP4 quantization, allowing the superchip to run large, dense models with a significantly reduced memory footprint.\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">By utilizing optimized libraries like llama.cpp with Multi-Token Prediction (MTP), the RTX Spark achieves up to a 3x performance gain, allowing dense 70B+ models to generate at over 20 t\u002Fs.\u003Cbr>\u003Cbr>\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Ch2 dir=\"ltr\">Market reality: Availability and pricing\u003C\u002Fh2>\r\n\u003Cp dir=\"ltr\">One practical insight from evaluating these setups is that the technically best option must always align with budget constraints and market availability.\u003C\u002Fp>\r\n\u003Cul>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">AMD Ryzen AI Max+ 395: Launched in January 2025, this platform is widely available and currently the more cost-effective choice. Fully functional 128 GB desktop workstations and laptops are available in the $2,700 to $3,400 range.\u003Cbr>\u003Cbr>\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" role=\"presentation\">NVIDIA RTX Spark: Scheduled for a Fall 2026 release, 128 GB consumer systems are projected to launch at a significant premium, with pricing expected to hover between $4,000 and $5,000. Lower-tier 64 GB models are expected to retail close to $4,000.\u003Cbr>\u003Cbr>\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Ch2 dir=\"ltr\">Outlook: Which edge workstation makes sense?\u003C\u002Fh2>\r\n\u003Cp dir=\"ltr\">The right choice depends on your specific performance priorities, budget, and real-world requirements. As the NVIDIA is still to be launched, the only option currently available is AMD. But when RTX Spark comes, this will be the verdict.\u003C\u002Fp>\r\n\u003Cp dir=\"ltr\">The AMD Ryzen AI Max+ 395 is an excellent option for developers looking for a cost-effective path to large local memory pools immediately. While the ROCm software stack requires some more manual configuration, developers can leverage the Vulkan backend and Linux TTM adjustments to bypass memory limits and smoothly run large models today.\u003C\u002Fp>\r\n\u003Cp dir=\"ltr\">The NVIDIA RTX Spark is the superior option for developers who prioritize software compatibility, native ecosystem support, and advanced agent security. With up to 300 GB\u002Fs of unified memory bandwidth and hardware-accelerated sandboxing via OpenShell, it provides a highly optimized environment—provided teams can accommodate the higher entry cost and slower compilation times of the ARM-based CPU.\u003Cbr>\u003Cbr>Our analysis will expand as the NVIDIA ecosystem matures; however, the Ryzen AI Max remains a robust and practical foundation for current local inference architectures.\u003C\u002Fp>\r\n\u003Cp dir=\"ltr\">[cta text=\"Free consulting\" to=\"\u002Fdigitalization\u002Fai-svetovanje\" color=\"yellow\"]\u003Cstrong id=\"docs-internal-guid-3905e05b-7fff-d41a-0e16-6134a4d5d4a0\">\u003C\u002Fstrong>\u003C\u002Fp>\r\n\u003Cp dir=\"ltr\">Also read: \u003C\u002Fp>\r\n\u003Cul>\r\n\u003Cli dir=\"ltr\" aria-level=\"1\">\u003Ca href=\"https:\u002F\u002Fkalmia.si\u002Fblog\u002Fai-logistics-agent-inventory-optimization\" target=\"_blank\" rel=\"noopener\">Logistics on Autopilot: How an AI Agent Solves Warehouse Chaos\u003C\u002Fa>\u003C\u002Fli>\r\n\u003Cli dir=\"ltr\" aria-level=\"1\">\r\n\u003Cp dir=\"ltr\" role=\"presentation\">\u003Ca href=\"https:\u002F\u002Fkalmia.xyz\u002Fblog\u002FOpenClaw-ai-agent-security\">OpenClaw, a new type of AI Agent—hot innovation or security risk?\u003C\u002Fa>\u003C\u002Fp>\r\n\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Cp dir=\"ltr\">\u003Cstrong id=\"docs-internal-guid-34937bff-7fff-0261-3b9d-3128e370473e\">\u003C\u002Fstrong>\u003Cbr>\u003Cbr>\u003C\u002Fp>","2026-06-04 13:37:32.461Z","https:\u002F\u002Fs3.eu-west-1.amazonaws.com\u002Fpocketbase-k-website\u002Fpbc_999467826\u002F73011udg1y7eh9q\u002Fai_max_bzwxd93vr1.png","73011udg1y7eh9q","en",1,"Comparing the AMD Ryzen AI Max+ 395 and NVIDIA RTX Spark for running local LLMs. Analyzing unified memory, software stacks, and edge AI workstation pricing.","practical-comparison-of-amd-ai-max-and-nvidia-rtx-spark","Local LLMs, AMD Ryzen AI Max+ 395, NVIDIA RTX Spark, Hardware Comparison, Edge AI, Pricing","RTX Sparks: A practical comparison of AMD Ryzen AI Max+ 395 and NVIDIA RTX Spark","2026-06-04 13:51:42.351Z",{"collectionId":4,"collectionName":5,"created":318,"details":319,"details_sl":323,"disabled":12,"file":17,"id":326,"key":327,"tag":328,"text":17,"text_sl":17,"updated":329},"2025-05-15 11:56:09.780Z",{"title":320,"text":321,"maxWidth":322},"Start your {i:project.}","Our expertise helps companies optimize processes, boost efficiency, and unlock the value of their data to drive long-term growth.","958px",{"title":324,"text":325,"maxWidth":322},"Začnimo {i:projekt.}","Naše izkušnje podjetjem pomagajo pri optimizaciji procesov, povečanju učinkovitosti in pridobivanju vrednosti iz podatkov za dolgoročno rast.","r49g939zg83h7hh","cta","page-enterprise","2025-05-20 14:23:21.739Z",{"collectionId":4,"collectionName":5,"created":331,"details":332,"details_sl":338,"disabled":12,"file":17,"id":344,"key":345,"tag":16,"text":17,"text_sl":17,"updated":346},"2025-05-16 09:14:56.631Z",{"title":333,"text":334,"button":335,"buttonUrl":194,"formTitle":336,"formButton":337},"Book a meeting directly","View available time slots and schedule a call with our representative.","Book a meeting with us","Not ready to book? 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