NVIDIA has announced a major leap forward in AI computing with the global rollout of its new DGX Spark and DGX Station™ systems, developed in partnership with Taiwan’s top computer manufacturers – Acer, GIGABYTE, and MSI.
These strategic collaborations will bring NVIDIA’s AI-first personal supercomputers to a broader global audience, delivering cutting-edge performance to developers, data scientists, researchers, startups, and government institutions. The compact systems are purpose-built to bring the full power of an AI server to the desktop – without sacrificing performance, scalability, or data privacy.
Designed to meet the demands of next-generation intelligent workloads, the systems address the growing need for local, high-performance AI platforms capable of managing proprietary data and supporting real-time model development.
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As agentic AI systems – those capable of autonomous reasoning and decision-making — continue to rise, the demand for AI infrastructure that supports rapid prototyping, fine-tuning, and inference is growing exponentially. Powered by the NVIDIA Grace Blackwell platform, the new DGX systems allow organizations to seamlessly scale AI workloads from the desktop to the data center.
“AI has revolutionized every layer of the computing stack — from silicon to software,” said Jensen Huang, founder and CEO of NVIDIA. “Direct descendants of the DGX-1 system that ignited the AI revolution, DGX Spark and DGX Station are created from the ground up to power the next generation of AI research and development.”
“There’s a clear shift among consumers and enterprises to prioritize systems that can handle the next generation of intelligent workloads,” said Michael Dell, chairman and CEO of Dell Technologies. “The interest in NVIDIA DGX Spark and NVIDIA DGX Station signals a new era of desktop computing, unlocking the full potential of local AI performance. Our portfolio is designed to meet these needs. Dell Pro Max with GB10 and Dell Pro Max with NVIDIA GB300 give organizations the infrastructure to integrate and tackle large AI workloads.”