{"id":6780,"date":"2025-10-13T10:44:13","date_gmt":"2025-10-13T10:44:13","guid":{"rendered":"https:\/\/openzeka.com\/en\/?page_id=6780"},"modified":"2026-02-13T11:47:54","modified_gmt":"2026-02-13T11:47:54","slug":"dgx-spark","status":"publish","type":"page","link":"https:\/\/openzeka.com\/en\/dgx-spark\/","title":{"rendered":"NVIDIA DGX Spark"},"content":{"rendered":"

NVIDIA DGX Spark<\/strong><\/h1><\/div>

A Grace Blackwell AI supercomputer on your desk.<\/p>\n<\/div>

<\/i> NVIDIA GB10 Superchip
\n<\/i> 128 GB of Coherent Unified System Memory
\n<\/i> NVIDIA ConnectX Networking
\n<\/i> NVIDIA AI Software Stack<\/p>\n<\/div><\/div><\/div><\/div><\/div>

NVIDIA DGX Spark<\/strong><\/h2><\/div>

Powered by the NVIDIA GB10 Grace Blackwell Superchip, NVIDIA DGX\u2122\u00a0Spark delivers 1 petaFLOP of AI performance in a power-efficient, compact form factor. With the NVIDIA AI software stack preinstalled and 128GB of memory, developers can prototype, fine-tune, and inference the latest generation of reasoning AI models from DeepSeek, Meta, Google, and others with up to 200 billion parameters locally, and seamlessly deploy to the data center or cloud.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

NVIDIA GPU, CPU, Networking, and AI Software Technologies<\/strong><\/h2><\/div><\/div><\/div>
<\/i>

NVIDIA GB10 Superchip<\/strong><\/h3><\/div>

Experience up to 1 petaFLOP of AI performance at FP4 precision with the NVIDIA Grace Blackwell architecture.<\/p>\n<\/div><\/div><\/div>

<\/i>

128 GB of Coherent Unified System Memory<\/strong><\/h3><\/div>

Run AI development and testing workloads with AI models up to 200 billion parameters at your desktop with large, unified system memory.<\/p>\n<\/div><\/div><\/div>

<\/i>

NVIDIA ConnectX Networking<\/strong><\/h3><\/div>

High-performance NVIDIA Connect-X networking enables connecting two NVIDIA DGX Spark systems together to work with AI models up to 405 billion parameters.<\/p>\n<\/div><\/div><\/div>

<\/i>

NVIDIA AI Software Stack<\/strong><\/h3><\/div>

Use a full-stack solution for generative AI workloads, including tools, frameworks, libraries, and pretrained models.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

Exploded View<\/strong><\/h3><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div>

Specifications<\/strong><\/h2><\/div>\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Architecture<\/strong><\/td>\nNVIDIA Grace Blackwell<\/td>\n<\/tr>\n
GPU<\/strong><\/td>\nNVIDIA Blackwell Architecture<\/td>\n<\/tr>\n
CPU<\/strong><\/td>\n20 core Arm, 10 Cortex-X925 + 10 Cortex-A725 Arm<\/td>\n<\/tr>\n
CUDA Cores<\/strong><\/td>\nNVIDIA Blackwell Generation<\/td>\n<\/tr>\n
Tensor Cores<\/strong><\/td>\n5th Generation<\/td>\n<\/tr>\n
RT Cores<\/strong><\/td>\n4th Generation<\/td>\n<\/tr>\n
Tensor Performance<\/strong><\/td>\n1 PFLOP<\/td>\n<\/tr>\n
System Memory<\/strong><\/td>\n128 GB LPDDR5x, unified system memory<\/td>\n<\/tr>\n
Memory Interface<\/strong><\/td>\n256-bit<\/td>\n<\/tr>\n
Memory Bandwidth<\/strong><\/td>\n273 GB\/s<\/td>\n<\/tr>\n
Storage<\/strong><\/td>\n1 or 4 TB NVME.M2 with self-encryption<\/td>\n<\/tr>\n
USB<\/strong><\/td>\n4x USB TypeC<\/td>\n<\/tr>\n
Ethernet<\/strong><\/td>\n1x RJ-45 connector 10 GbE<\/td>\n<\/tr>\n
NIC<\/strong><\/td>\nConnectX-7 Smart NIC<\/td>\n<\/tr>\n
Wi-Fi<\/strong><\/td>\nWiFi 7<\/td>\n<\/tr>\n
Bluetooth<\/strong><\/td>\nBT 5.3<\/td>\n<\/tr>\n
Audio-output<\/strong><\/td>\nHDMI multichannel audio output<\/td>\n<\/tr>\n
Power Consumption<\/strong><\/td>\nTBD<\/td>\n<\/tr>\n
Display Connectors<\/strong><\/td>\n1x HDMI 2.1a<\/td>\n<\/tr>\n
NVENC | NVDEC<\/strong><\/td>\n1x | 1x<\/td>\n<\/tr>\n
OS<\/strong><\/td>\nNVIDIA DGX\u2122\u00a0OS<\/td>\n<\/tr>\n
System Dimensions<\/strong><\/td>\n150 mm L x 150 mm W x 50.5 mm H<\/td>\n<\/tr>\n
System Weight<\/strong><\/td>\n1.2 kg<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

View Datasheet<\/strong> <\/i><\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"100-width.php","meta":{"footnotes":""},"class_list":["post-6780","page","type-page","status-publish","hentry"],"yoast_head":"\nNVIDIA DGX Spark - OpenZeka | NVIDIA Embedded Distributor<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/openzeka.com\/en\/dgx-spark\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NVIDIA DGX Spark\" \/>\n<meta property=\"og:url\" content=\"https:\/\/openzeka.com\/en\/dgx-spark\/\" \/>\n<meta property=\"og:site_name\" content=\"OpenZeka | NVIDIA Embedded Distributor\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/openzeka\" \/>\n<meta property=\"article:modified_time\" content=\"2026-02-13T11:47:54+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@openzeka\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/openzeka.com\/en\/dgx-spark\/\",\"url\":\"https:\/\/openzeka.com\/en\/dgx-spark\/\",\"name\":\"NVIDIA DGX Spark - 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