NVIDIA DGX Spark 4TB

Key features of the NVIDIA DGX Spark 4TB mini computer

Data centre performance in a miniature body

At the heart of the NVIDIA DGX Spark is the GB10 Grace Blackwell superchip – a unique combination of a 20-core ARM processor (10× Cortex-X925 + 10× Cortex-A725) and a Blackwell architecture GPU with 5th generation tensor cores and 4th generation RT cores. Both parts of the superchip are connected by NVLink-C2C technology, which provides 5× the throughput of PCIe 5th generation and allows the CPU and GPU to share a single 128GB pool of unified LPDDR5X memory. The result is tremendous performance that consumes a maximum of 240W and fits on any desk. This shared memory, without the traditional separation of GPU VRAM and system RAM, is the key advantage of the DGX Spark over standard desktop setups. Where a GeForce RTX 5090 graphics card offers 32GB of memory, the DGX Spark provides four times as much for AI models, without needing to quantize or split the models across multiple devices.

A personal supercomputer that fits on any desk

The NVIDIA DGX Spark proves you no longer need a server room or a rack full of hardware to run the largest AI models. With dimensions of 150×150×50.5mm and a weight of 1.2kg, the DGX Spark fits on any desk next to a laptop, yet offers performance that just a few years ago required an entire server full of graphics cards. Four USB-C 4.0 ports with speeds up to 40Gb/s, Wi-Fi 7, 10Gb Ethernet, and an HDMI output ensure all your accessories can be connected without needing an external hub or docking station. The DGX Spark is designed for developers, scientists, and AI professionals who want the full power of a local AI personal supercomputer without relying on the cloud – right where they work.

Limitless prototyping and development of AI applications

The complete NVIDIA AI software stack provides developers with a comprehensive platform for creating AI models, AI agents, and AI-enhanced applications – all locally, without relying on the cloud. Once a solution is ready for deployment or final fine-tuning, the DGX Spark enables direct and seamless migration of workloads to the NVIDIA DGX Cloud or other NVIDIA-accelerated infrastructure.

Fine-tune AI models with up to 70 billion parameters

Use the 128GB of unified memory in the NVIDIA DGX Spark to fine-tune pre-trained models with up to 70 billion parameters directly at your workstation. Training on your own data lets you specialise AI models for specific needs, industry data, or particular use cases – without having to send sensitive data to the cloud or pay for expensive GPU instances. The result is a model tailored precisely to your scenario, created locally, securely, and under your full control.

NVIDIA DGX Spark 4TB delivers top-level data science

The combination of 128GB of unified memory and 1 PFLOP of parallel throughput performance makes the NVIDIA DGX Spark the ideal workstation for demanding data analytics and machine learning projects. Extensive data sets, complex computational models, and demanding training and analysis processes that previously required a powerful cloud cluster or dedicated server now run directly on your desk. Fast, local, and with no waiting for remote infrastructure.

Inference for models with up to 200 billion parameters

The 5th generation Tensor Cores with FP4 format support achieve up to 1 PFLOP of performance and, combined with 128GB of system memory, allow you to run inference on state-of-the-art AI models with up to 200 billion parameters right on your desk. Test, validate, and deploy models like Llama, Gemma, or Qwen locally and in real time – without cloud latency, shared infrastructure, or the risk of sensitive data leaks.

Application development for robotics, industry, and smart cities

NVIDIA DGX Spark is an exceptional platform for developing robotics systems, smart city solutions, and computer vision applications. Pre-installed NVIDIA frameworks – Isaac for robotics, Metropolis for video analysis, and Holoscan for real-time data processing – enable developers to fully harness the power of DGX Spark and rapidly prototype and deploy applications locally, without dependency on cloud infrastructure and with full support from the NVIDIA ecosystem.