Nvidia DGX Spark
NVIDIA DGX Spark
Powered by the NVIDIA GB10 Grace Blackwell Superchip, NVIDIA DGX™ Spark delivers one petaFLOP1 of AI performance in a power-efficient, compact form factor. With the NVIDIA AI software stack preinstalled and 128 GB of memory, developers can prototype, fine-tune, and inference the latest generation of reasoning AI models from DeepSeek, Meta, NVIDIA, Google, Qwen and others with up to 200 billion parameters locally.
1 Theoretical FP4 TOPS using the sparsity feature.
[5/5] Currently we have yet to obtain pricing on the DGX Spark. You will be notified through the signup when pricing or inventory is available.
Features
NVIDIA GB10 Superchip
Experience up to 1 petaFLOP of AI performance at FP4 precision with the NVIDIA Grace Blackwell architecture.
128GB of Coherent Unified System Memory
Run AI development and testing workloads with AI models up to 200 billion parameters at your desktop with a large, unified system memory.
NVIDIA ConnectX Networking
High-performance NVIDIA ConnectX™ networking enables the connection of two NVIDIA DGX Spark systems to work with AI models of up to 405 billion parameters.
NVIDIA AI Software Stack
Utilize a full-stack solution for generative AI workloads, encompassing NVIDIA tools, frameworks, libraries, and pre-trained models including NVIDIA NIM.
Accelerate All AI Workloads
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.
Prototyping
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-Tuning
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.
Inference
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.
Data Science
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.
Edge Applications
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
Specifications
| Architecture | NVIDIA Grace Blackwell |
| GPU | NVIDIA Blackwell Architecture |
| CPU | 20 core Arm, 10 Cortex-X925 + 10 Cortex-A725 Arm |
| CUDA Cores | NVIDIA Blackwell Generation |
| Tensor Cores | 5th Generation |
| RT Cores | 4th Generation |
| Tensor Performance1 | 1000 AI TOPS |
| System Memory | 128 GB LPDDR5x, unified system memory |
| Memory Interface | 256-bit |
| Memory Bandwidth | 273 GB/s |
| Storage | 1 or 4 TB NVME.M2 with self-encryption |
| USB | 4x USB Type C |
| Ethernet | 1x RJ-45 connector 10 GbE |
| NIC | ConnectX-7 Smart NIC |
| Wi-Fi | WiFi 7 |
| Bluetooth | BT 5.3 |
| Audio-output | HDMI multichannel audio output |
| Power Consumption | TBD |
| 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 |
* preliminary specifications, subject to change
1 Theoretical FP4 TOPS using the sparsity feature.
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