
The world’s smallest desktop AI supercomputer is now available in the form of the NVIDIA DGX™ Spark. Originally announced in January 2025 under the name Digits, NVIDIA CEO Jensen Huang praised Spark as a tool for data scientists, AI researchers, and students “to engage and shape the age of AI.”
Powered by a 240 watt power supply that uses a standard wall outlet and with a golden asteroid-like textured casing, it looks more like a high-end PC than an AI supercomputer. Only an inch wider and longer than the M4 Mac Mini, Spark measures just 6” x 6” x 2” and weighs just 2.6 pounds. This compact size makes it all the more impressive that it can fuel enterprise AI development with ease. If you’re looking for a mini AI companion to run local AI models, the NVIDIA DGX Spark is the best-looking, most-talked-about option on the market. But does it deliver on the hype?
Spark runs on a 20-core ARM64 CPU and a NVIDIA GB10 Grace Blackwell GPU. It’s capable of handling AI models with up to 200 billion parameters at a million billion calculations per second. Its 128GB of unified memory can be seamlessly allocated to both its CPU and GPU without static partitioning. Plus, its stock 4TB NVME.M2 SSD hard drive provides more storage than many of its closest competitors.
With four USB-C ports and an HDMI port, you can plug it into an array of peripheries, monitors, or even external GPUs. You can also connect two Sparks together and run them simultaneously to double your performance.
Specification | NVIDIA DGX Spark |
|---|---|
Architecture | NVIDIA Grace Blackwell |
| GPU | Blackwell Architecture |
| CPU | 20 core Arm, 10 Cortex-X925 + 10 Cortex-A725 Arm |
CUDA Cores | Blackwell Generation |
Tensor Performance1 | 1 PFLOP |
System Memory | 128 GB LPDDR5x, coherent unified system memory |
| OS | NVIDIA DGX™ OS |
System Dimensions | 150 mm L x 150 mm W x 50.5 mm H |
See full specs at NVIDIA.com.
Following a delayed launch, Spark was released earlier this month. It is available on NVIDIA.com and other retailers for $3,999. However, Spark was initially slated to retail for only $2,999 — the price of the similarly spec’d ASUS Ascent GX10. Though the Ascent comes with only a 1TB hard drive and is far less inspired design-wise. Beyond its delayed release and initial sticker shock, early reviews have been mixed.
Many early adopters praise Spark’s ability to run larger AI models and champion the GP10’s ability to outperform many PC GPUs. Additionally, remote access through tools like Tailscale have made Spark even more accessible. Using it as a companion to developers’ existing PCs and other devices, you can work with your Spark from anywhere on earth.
However, Spark is being criticized for not reaching its listed capabilities. Though rated for 240 watts, noted programmer and video game developer John Carmack reported less than half of that. As a result, he suggested it only delivers about half of its advertised performance. With wide reports of latency, throttling, and reboot issues, some shoppers have voiced reservations about buying it for more data-intensive projects.
For now, Spark’s full range of ideal use cases have yet to be explored and its ecosystem is still evolving. Recent news that open source platforms like NVIDIA Sionna Research Kit and Aerial Testbed are available for the DGX network could provide Spark with native tools to accelerate its capabilities. It has shined brightest for agentic AI and reasoning models. For a mini AI supercomputer, it does a lot — it may just not be enough for everyone.
Spark's steep price tag makes it an expense that may be hard to justify. While on-premise, local AI development sounds great, Spark may be too niche for wide adoption at its price point. But the tiny supercomputer does nonetheless reflect a push toward making advanced AI computing more widely available.
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