A Grace Blackwell AI supercomputer on your desk.
Delivering the power of an AI supercomputer in a desktop-friendly size, NVIDIA DGX Spark is ideal for AI developer, researcher, and data scientist workloads.
Develop, test, and validate AI models and applications.
With the NVIDIA AI software stack, NVIDIA DGX Spark provides the platform for developers to create, test, and validate AI models and AI-augmented applications and solutions. For final tuning or deployment, conveniently evaluate work for eventual migration to NVIDIA DGX cloud or other NVIDIA accelerated data centers or cloud infrastructures.
Fine-tune AI models up to 70 billion parameters.
Improve the performance of pre-trained models by fine-tuning on NVIDIA DGX Spark. With 128GB of unified system memory, fine-tune models up to 70 billion parameters to customize AI models and solutions for specific needs and use cases.
High-performance data science at your desk.
NVIDIA DGX Spark’s combination of 128GB of unified memory and 1 petaFLOP of parallel throughput maximizes performance of large, computationally complex data analytics and machine learning workflows at your desk.
Test, validate, and inference with AI models up to 200 billion parameters.
Fifth-generation Tensor Cores with support for FP4 deliver up to 1 petaFLOP of AI computing performance, combined with 128GB of system memory, accelerate inference of state-of-the-art AI models to test, validate and deploy from your NVIDIA DGX Spark.
Develop edge applications with NVIDIA AI frameworks, including Isaac™, Metropolis, and many others.
NVIDIA DGX Spark offers an exceptional platform for developing robotics, smart city, and computer vision solutions. NVIDIA frameworks include Isaac, Metropolis, and Holoscan. These frameworks and tools enable developers to take advantage of the power of NVIDIA DGX Spark to quickly develop edge applications.
NVIDIA DGX Spark systems include NVIDIA ConnectX® networking, enabling developers to connect two systems for inference and fine-tuning of AI models up to 405 billion parameters. This configuration is optimized for local-scale deployments of large models like Llama 3.1, helping developers accelerate iteration without the cloud.
With shared architecture across the DGX platform, workloads built on NVIDIA DGX Spark can be deployed seamlessly to the data center or NVIDIA DGX™ Cloud when needed.
| Architecture | NVIDIA Grace Blackwell |
| GPU | Blackwell Architecture |
| CPU | 20 core Arm, 10 Cortex-X925 + 10 Cortex-A725 Arm |
| CUDA Cores | Blackwell Generation |
| Tensor Cores | 5th Generation |
| RT Cores | 4th Generation |
| Tensor Performance1 | 1 PFLOP |
| System Memory | 128 GB LPDDR5x, coherent unified system memory |
| Memory Interface | 256-bit |
| Memory Bandwidth | 273 GB/s |
| Storage | 4 TB NVME.M2 with self-encryption |
| USB | 4x USB TypeC |
| Ethernet | 1x RJ-45 connector 10 GbE |
| NIC | ConnectX-7 NIC @ 200 Gbps |
| Wi-Fi | WiFi 7 |
| Bluetooth | BT 5.4 |
| Audio-output | HDMI multichannel audio output |
| Power Supply | 240 W |
| GB10 TDP2 | 140 W |
| Display Connectors | 1x HDMI 2.1a |
| NVENC | NVDEC | 1x | 1x |
| OS | NVIDIA DGX™ OS |
| System Dimensions | 150 mm L x 150 mm W x 50.5 mm H |
| System Weight | 1.2 kg |
A day before the DGX Spark began shipping worldwide, Jensen Huang, founder and CEO of NVIDIA, personally delivered one of the first units of the device to Elon Musk at Starbase in Texas. The event was a reference to the origins of DGX supercomputers - in 2016, Huang also personally handed over the first NVIDIA DGX-1 system to Musk, who at the time was running OpenAI.
| Full technical specification | |
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| Free Shipping | No |
