Nvidia announces $3,000 personal AI supercomputer called Digits

If you were looking for your own personal AI supercomputer, Nvidia has you covered.

The chipmaker announced at CES it’s launching a personal AI supercomputer called Project Digits in May. The heart of Project Digits is the new GB10 Grace Blackwell Superchip, which packs enough processing power to run sophisticated AI models while being compact enough to fit on a desk and run from a standard power outlet (this kind of processing power used to require much larger, more power-hungry systems). This desktop-sized system can handle AI models with up to 200 billion parameters, and has a starting price of $3,000. The product itself looks a lot like a Mac Mini.

“AI will be mainstream in every application for every industry. With Project Digits, the Grace Blackwell Superchip comes to millions of developers,” Nvidia CEO Jensen Huang said in a press release. “Placing an AI supercomputer on the desks of every data scientist, AI researcher and student empowers them to engage and shape the age of AI.”

Each Project Digits system comes equipped with 128GB of unified, coherent memory (by comparison, a good laptop might have 16GB or 32GB of RAM) and up to 4TB of NVMe storage. For even more demanding applications, two Project Digits systems can be linked together to handle models with up to 405 billion parameters (Meta’s best model, Llama 3.1, has 405 billion parameters).

The GB10 chip delivers up to 1 petaflop of AI performance (which means it can perform 1 quadrillion AI calculations per second) at FP4 precision (which helps make the calculations faster by making approximations), and the system features Nvidia’s latest-generation CUDA cores and fifth-generation Tensor Cores, connected via NVLink-C2C to a Grace CPU containing 20 power-efficient Arm-based cores. MediaTek, known for their Arm-based chip designs, collaborated on the GB10’s development to optimize its power efficiency and performance.

Users will also get access to Nvidia’s AI software library, including development kits, orchestration tools, and pre-trained models available through the Nvidia NGC catalog. The system runs on Linux-based Nvidia DGX OS and supports popular frameworks like PyTorch, Python, and Jupyter notebooks. Developers can fine-tune models using the Nvidia NeMo framework and accelerate data science workflows with Nvidia RAPIDS libraries.

Users can develop and test their AI models locally on Project Digits, then deploy them to cloud services or data center infrastructure using the same Grace Blackwell architecture and Nvidia AI Enterprise software platform.

Nvidia offers a range of similar devices in the same accessibility style — in December, it announced a $249 version of its Jetson computer for AI applications, targeting hobbyists and startups, called the Jetson Orin Nano Super (it handles models up to 8 billion parameters).