
The world’s smallest desktop AI supercomputer is now available in the form of the NVIDIA DGX™ Spark. Originally [announced in January 2025](https://www.theverge.com/news/798775/nvidia-spark-personal-ai-supercomputer) 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?

### **Inside the NVIDIA DGX Spark**

Spark runs on a 20-core ARM64 CPU and a NVIDIA GB10 [Grace Blackwell](https://vast.ai/article/report-nvidia-developing-new-blackwell-based-ai-chip-for-china) 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.

#### NVIDIA DGX Spark Technical Specifications

<table class="w-full border-collapse border border-gray-300">
    <thead>
        <tr class="bg-gray-100">
            <th class="border border-gray-300 px-4 py-2 text-left font-semibold">
                Specification
            </th>
            <th class="border border-gray-300 px-4 py-2 text-left font-semibold">
                NVIDIA DGX Spark
            </th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Architecture
            </td>
            <td class="border border-gray-300 px-4 py-2">
                NVIDIA Grace Blackwell
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">GPU</td>
            <td class="border border-gray-300 px-4 py-2">
                Blackwell Architecture
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">CPU</td>
            <td class="border border-gray-300 px-4 py-2">
                20 core Arm, 10 Cortex-X925 + 10 Cortex-A725 Arm
            </td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                CUDA Cores
            </td>
            <td class="border border-gray-300 px-4 py-2">
                Blackwell Generation
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                Tensor Performance<sup>1</sup>
            </td>
            <td class="border border-gray-300 px-4 py-2">1 PFLOP</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                System Memory
            </td>
            <td class="border border-gray-300 px-4 py-2">
                128 GB LPDDR5x, coherent unified system memory
            </td>
        </tr>
        <tr>
            <td class="border border-gray-300 px-4 py-2 font-semibold">OS</td>
            <td class="border border-gray-300 px-4 py-2">NVIDIA DGX™ OS</td>
        </tr>
        <tr class="bg-gray-50">
            <td class="border border-gray-300 px-4 py-2 font-semibold">
                System Dimensions
            </td>
            <td class="border border-gray-300 px-4 py-2">
                150 mm L x 150 mm W x 50.5 mm H
            </td>
        </tr>
    </tbody>
</table>

See full specs at [NVIDIA.com](https://www.nvidia.com/en-us/products/workstations/dgx-spark/).

### **Should You Buy the NVIDIA DGX Spark?**

Following a delayed launch, Spark was released earlier this month. It is [available on](https://www.nvidia.com/en-us/products/workstations/dgx-spark/) [NVIDIA.com](http://NVIDIA.co) 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](https://www.pcgamer.com/hardware/graphics-cards/og-doom-legend-john-carmack-says-his-nvidia-dgx-spark-box-has-thermal-throttling-issues-raising-doubts-over-the-viability-of-nvidias-ai-apu-for-laptop-pcs/) 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](https://blogs.nvidia.com/blog/open-source-aerial-ai-native-6g/) 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.

### **Bringing Powerful Compute Within Reach**

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.

Here at Vast.ai, we share that goal of making AI development accessible. Our mission is to democratize AI by making powerful GPUs available to anyone, anywhere — without the upfront cost of purchasing and maintaining costly hardware. With our cloud GPU rental platform, you can leverage machines that exceed DGX Spark with affordable, on-demand compute power designed to scale with your needs.

After all, democratizing AI starts with access. On Vast, you'll save 5–6X compared to traditional cloud providers. Find your compute and [get started](https://vast.ai/pricing) today\!


Explore the NVIDIA DGX Spark — the world’s smallest desktop AI supercomputer — its specs, performance, reviews, and how Vast.ai offers a more scalable alternative.

Posts about: Cloud Gpu Computing

The NVIDIA DGX Spark: The New Desktop AI Supercomputer That Fits in Your Hand