Nvidia unveils $3,000 personal AI supercomputer at CES

Nvidia has unveiled Project DIGITS, a $3,000 personal AI supercomputer that will be available in May 2025, bringing unprecedented AI computing power to individual desktops.

Product specifications and capabilities: Announced at CES 2025, Project DIGITS represents a significant advancement in personal AI computing, powered by Nvidia’s new GB 10 Grace Blackwell Superchip.

• The device features 128GB of unified memory and 4TB of storage
• It can run AI models with up to 200 billion parameters locally
• Users can link two units together to handle models with up to 405 billion parameters
• The system delivers petaflop-level AI computing performance

Market positioning and use cases: The personal supercomputer targets developers and researchers who need powerful local AI processing capabilities.

• Primary users will be developers testing new AI applications
• The system enables AI video and image content generation
• Companies can use it to prototype and fine-tune models before cloud deployment
• The price point of $3,000 makes it comparable to high-end gaming laptops

Strategic importance: Nvidia’s launch comes amid increasing competition in the AI hardware market.

• The release helps defend Nvidia’s CUDA framework against competing platforms
• This follows Nvidia’s earlier entry-level offering, the $249 Jetson, which handles 8 billion parameter models
• The product demonstrates Nvidia’s commitment to democratizing AI development tools

Executive perspective: Nvidia’s leadership emphasizes the transformative potential of personal AI computing.

• CEO Jensen Huang states that “AI will be mainstream in every application for every industry”
• Huang positions Project DIGITS as empowering “every data scientist, AI researcher and student” to shape AI’s future

Market implications: Project DIGITS represents a significant shift in AI development capabilities, potentially disrupting the traditional reliance on cloud infrastructure for advanced AI model development and testing. This move could accelerate AI innovation by removing barriers to entry for smaller organizations and individual developers who previously lacked access to supercomputer-level resources.