- Nvidia DGX Spark runs larger AI models locally using massive aggregate 128 GB memory efficiently
- Native CUDA support makes Spark ideal for advanced AI workloads on desktops
- Its Arm CPU and Blackwell GPU combination avoids expensive professional graphics cards
The long-awaited Nvidia DGX Spark has finally arrived as a very small desktop system built around the GB10 Superchip.
It has a shared 128GB of LPDDR5X memory, a specification that immediately sets the system apart from typical desktops and even most compact workstations.
And according to an early review of Tom’s hardwarethe system only delivers strong results when its AI-oriented capabilities are fully utilized.
Hardware design and connectivity focus
Spark’s hardware design relies on a single package that combines an Arm-based CPU with a Blackwell GPU.
This integration allows Nvidia to support larger local models without requiring professional-grade graphics cards at extreme costs.
While Apple and AMD systems offer large shared memory configurations, they lack direct support for Nvidia’s software ecosystem, which continues to dominate many AI development workflows.
The physical design emphasizes density and airflow rather than visual flair or modular expansion.
At just over a liter in volume, measuring around 150 by 150 by 50mm, the device fits comfortably among any modern mini PC, but the similarities mostly end there.
In addition to a USB-C power input, the device has three USB-C 20 Gbps ports with DisplayPort alternate mode, an HDMI 2.1a port and a 10 Gb Ethernet connection.
Most notably, it includes two QSFP ports powered by a built-in ConnectX-7 network interface capable of up to 200 Gbps, allowing multiple devices to be connected for distributed computing experiments, a feature rarely associated with a mini PC.
The system runs DGX OS, a custom Ubuntu 24.04 LTS distribution closely aligned with Nvidia’s software stack.
It can function as a locally attached computer with a monitor and keyboard or as a headless system that can be accessed remotely via a network.
Nvidia’s Sync tool simplifies remote access from Windows and macOS machines, allowing AI tools to run continuously in the background.
These usage patterns are similar to how mobile workstations or shared computing nodes are accessed, rather than how everyday desktops are typically used.
DGX Spark benefits from a total 128GB memory pool with built-in CUDA support, a pairing unusual in compact systems designed for local AI work.
This configuration allows larger models to run fully in memory, avoiding frequent data movement between system RAM and GPU memory, thereby reducing some of the practical limitations seen on discrete GPUs with smaller VRAM pools.
The same ability also introduces clear trade-offs. The price of entry remains high compared to compact desktops, especially for users who don’t run demanding AI workloads every day.
The system does not support Windows, which limits software compatibility for users outside of Linux-focused environments.
Its GPU is also unsuitable for gaming or general graphics tasks, reinforcing its narrow scope.
DGX Spark assumes that local AI experimentation is a primary and ongoing requirement, but if this is not your priority, it loses practical value.
Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews and opinions in your feeds. Be sure to click the Follow button!
And of course you can too follow TechRadar on TikTok for news, reviews, video unboxings, and get regular updates from us on WhatsApp also.
