{"product_id":"msi-edgexpert-nvidia-dgx-gb10-ai-supercomputer-128gb-4tb","title":"MSI EdgeXpert NVIDIA DGX GB10 AI Supercomputer 128GB 4TB","description":"\u003cp\u003eThe MSI EdgeXpert NVIDIA DGX GB10 AI Supercomputer is a desktop AI platform powered by the NVIDIA Grace Blackwell Superchip, built for developers and researchers who need to run large language models and AI workloads locally instead of relying on cloud infrastructure. It delivers 1 petaFLOP of AI performance in a compact form factor, combining an Arm 20-core CPU with NVIDIA Blackwell GPU architecture to handle model development, fine-tuning, and inference at scale.\u003c\/p\u003e\u003ch5\u003eWhy This Product\u003c\/h5\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e1 petaFLOP AI performance in a desktop\u003c\/strong\u003e — Delivers 1000 AI TOPS (FP4) tensor throughput via NVIDIA Grace Blackwell, enabling real-time processing of complex AI models without cloud dependency.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e128 GB unified system memory\u003c\/strong\u003e — LPDDR5x coherent memory shared between CPU and GPU via NVLink-C2C interconnect, five times the bandwidth of PCIe 5.0 for fast data throughput during model operations.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eScales to 512 GB total VRAM\u003c\/strong\u003e — Stack up to four units via ConnectX-7 networking to handle models reaching 405 billion parameters, with switchless ring topology for 3-node or star topology for 4-node clusters.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e4 TB encrypted NVMe storage\u003c\/strong\u003e — Self-encrypting M.2 drive for storing datasets, model checkpoints, and inference data with integrated security.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eComplete hardware and software stack\u003c\/strong\u003e — Ships with NVIDIA DGX OS and all software components for agents, multimodal inference, data science, and edge AI development—no separate software licensing required.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch5\u003eAt a Glance\u003c\/h5\u003e\u003ctable style=\"width:100%;border-collapse:collapse;font-size:14px;margin-bottom:10px;\"\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eAI Tensor Performance\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e1 petaFLOP (FP4)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eSystem Memory\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e128 GB LPDDR5x unified\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eStorage\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e4 TB NVMe.M2 encrypted\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eProcessor\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003eNVIDIA Grace Blackwell (GB10) + Arm 20-core\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eDimensions\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e151 × 151 × 52 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eWeight\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e1.2 kg\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003ePower\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e240W PSU (GB10 TDP 140W)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\u003ch5\u003eCompatibility\u003c\/h5\u003e\u003cul\u003e\n\u003cli\u003e✓ Runs NVIDIA DGX OS with full support for CUDA, NVIDIA TensorRT, and standard AI frameworks (PyTorch, TensorFlow).\u003c\/li\u003e\n\u003cli\u003e✓ Connects via 1× RJ-45 10 GbE Ethernet, 4× USB 3.2 Type-C, HDMI 2.1a, and WiFi 7 \/ Bluetooth 5.3.\u003c\/li\u003e\n\u003cli\u003e✓ Scales via ConnectX-7 Smart NIC and NVIDIA NVLink for multi-unit clustering without requiring external Ethernet switches for 3-node setups.\u003c\/li\u003e\n\u003cli\u003eNote: 4-node clustering requires a 200 GbE QSFP Ethernet switch; 2 and 3-node clusters use direct QSFP DAC cables.\u003c\/li\u003e\n\u003cli\u003e✗ Not compatible with standard x86 or x64 software—requires ARM-native or containerised applications designed for NVIDIA DGX platforms.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch5\u003eBest For \/ Trade-offs\u003c\/h5\u003e\u003cp\u003e\u003cstrong\u003eBest for:\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eLarge language model inference and fine-tuning\u003c\/li\u003e\n\u003cli\u003eMultimodal AI research and development\u003c\/li\u003e\n\u003cli\u003eEdge AI deployment without cloud latency\u003c\/li\u003e\n\u003cli\u003eData science and model experimentation locally\u003c\/li\u003e\n\u003cli\u003eOrganisations scaling from single-unit to multi-unit AI clusters\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003eTrade-offs:\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eExisting x86 software stacks\u003c\/b\u003e — runs Arm-native only; legacy applications built for x64 or x86 will not run without recompilation or containerisation.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eGraphics-intensive or gaming workloads\u003c\/b\u003e — optimised exclusively for AI tensor compute; no consumer GPU drivers or gaming support.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eTeams without GPU development expertise\u003c\/b\u003e — full-stack AI platform requires knowledge of CUDA, DGX OS, and containerised deployment patterns.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eBudget-constrained projects\u003c\/b\u003e — enterprise-grade AI supercomputer positioned at the high end of desktop AI performance.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch5\u003eDescription\u003c\/h5\u003e\u003cp\u003eMSI EdgeXpert is a desktop AI supercomputer built on the NVIDIA DGX Spark platform, powered by the NVIDIA GB10 Grace Blackwell Superchip. It delivers up to 1,000 AI TOPS (FP4) of performance in a compact 151 x 151 x 52 mm chassis weighing 1.2 kg, letting developers run large AI models locally instead of in the cloud.\u003c\/p\u003e\u003cp\u003eEdgeXpert is designed from the ground up to build and run AI. Powered by the NVIDIA GB10 Grace Blackwell Superchip, it is a complete local AI platform for agents, multimodal inference, fine-tuning, data science, and edge development, bringing the power of NVIDIA Grace Blackwell to every desk. Its compact design facilitates seamless integration and deployment, enabling powerful AI performance for innovators who demand excellence.\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eNVIDIA® Grace Blackwell (GB10) Architecture: NVIDIA Blackwell GPU \u0026amp; Arm 20-core CPU, NVIDIA® NVLink®-C2C CPU-GPU memory interconnect\u003c\/li\u003e\n\u003cli\u003e64 GB or 128 GB LPDDR5x coherent, unified system memory\u003c\/li\u003e\n\u003cli\u003e1000 AI TOPS (FP4) AI performance\u003c\/li\u003e\n\u003cli\u003eFull stack solution, hardware \u0026amp; software, designed for AI developers\u003c\/li\u003e\n\u003cli\u003eConnectX-7 networking: Scales out from 2 to 4 EdgeXpert systems, expanding total VRAM up to 128 GB~512 GB — 3-node cluster: ring topology via 3 QSFP DAC cables (switchless); 4-node cluster: star topology via a 200 GbE QSFP Ethernet switch\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThe MSI EdgeXpert AI Supercomputer transforms the landscape of desktop AI performance, offering petaflop-scale power via the next-generation NVIDIA® GB10 Grace Blackwell Superchip—the same engine driving the NVIDIA DGX™.\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eNVIDIA® Grace Blackwell Architecture\u003c\/strong\u003e — NVIDIA Blackwell GPU \u0026amp; Arm 20 core CPU streamlines data preprocessing and orchestration to speed up model optimisation and deliver real-time performance.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNVLink®-C2C Technology\u003c\/strong\u003e — Delivers a unified CPU+GPU memory architecture with up to five times the bandwidth of PCIe 5.0, enabling lightning-fast data throughput.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUnmatched AI Computing Power\u003c\/strong\u003e — 1000 AI TOPS (FP4) Tensor Performance provides exceptional speed to seamlessly handle demanding AI workloads at scale.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e128 GB LPDDR5x, unified system memory\u003c\/strong\u003e — Offers expansive memory capacity for seamless model development, fast experimentation, and efficient inference performance.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStack via NVIDIA ConnectX\u003c\/strong\u003e — High-speed NVIDIA ConnectX networking links two MSI systems to handle AI workloads with models reaching up to 405 billion parameters.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch5\u003eSpecifications\u003c\/h5\u003e\u003cp\u003e\u003cstrong\u003eProcessor \u0026amp; Memory\u003c\/strong\u003e\u003c\/p\u003e\u003ctable style=\"width:100%;border-collapse:collapse;font-size:14px;margin-bottom:10px;\"\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eBrand\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003eMSI\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eCPU\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e20-core Arm (10 Cortex-X925 + 10 Cortex-A725)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003eNVIDIA Blackwell Generation\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eTensor Cores\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e5th Generation\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eRT Cores\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e4th Generation\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eSystem Memory\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e128 GB LPDDR5x, unified system memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eMemory Interface\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e256-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e273 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eCPU-GPU Interconnect\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003eNVIDIA NVLink-C2C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\u003cp\u003e\u003cstrong\u003ePerformance \u0026amp; Storage\u003c\/strong\u003e\u003c\/p\u003e\u003ctable style=\"width:100%;border-collapse:collapse;font-size:14px;margin-bottom:10px;\"\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eAI Tensor Performance\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e1 petaFLOP AI Performance (FP4, Sparse)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eStorage\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e4 TB NVMe.M2 with self-encryption\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eVideo Encoding \/ Decoding\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e1× NVENC | 1× NVDEC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\u003cp\u003e\u003cstrong\u003eConnectivity\u003c\/strong\u003e\u003c\/p\u003e\u003ctable style=\"width:100%;border-collapse:collapse;font-size:14px;margin-bottom:10px;\"\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eEthernet\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e1× RJ-45 10 GbE\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eSmart NIC\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003eConnectX-7\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eUSB\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e4× USB 3.2 Type-C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eDisplay\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e1× HDMI 2.1a\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eWi-Fi\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003eWiFi 7\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eBluetooth\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003eBT 5.3\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eAudio Output\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003eHDMI multichannel audio output\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\u003cp\u003e\u003cstrong\u003ePhysical \u0026amp; Power\u003c\/strong\u003e\u003c\/p\u003e\u003ctable style=\"width:100%;border-collapse:collapse;font-size:14px;margin-bottom:10px;\"\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eDimensions\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e151 × 151 × 52 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eWeight\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e1.2 kg\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003ePower Supply\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e240W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eGB10 TDP\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003e140W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;font-weight:bold;width:35%;\"\u003eOperating System\u003c\/td\u003e\n\u003ctd style=\"padding:8px 12px;border:1px solid #ddd;\"\u003eNVIDIA DGX OS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"MSI","offers":[{"title":"Default Title","offer_id":55697035592056,"sku":"MSI-7381","price":6601.02,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0690\/9119\/7218\/files\/e449a495f236185a7bea9acbf461425ea3632e87.jpg?v=1792012125","url":"https:\/\/tech-syndicate.co.uk\/products\/msi-edgexpert-nvidia-dgx-gb10-ai-supercomputer-128gb-4tb","provider":"Tech Syndicate","version":"1.0","type":"link"}