System Overview#

Powered by the NVIDIA Grace Blackwell architecture, DGX Spark enables developers, researchers, and data scientists to prototype, deploy, and fine-tune large AI models on their desktop.

Flexible Deployment Options#

The DGX Spark is designed for maximum flexibility in how you use it:

  • Desktop Mode: Use with keyboard, mouse, and monitor for local development work

  • Network Appliance Mode: Operate headless for remote access and server-style deployments

Both modes are fully supported and equally capable, allowing you to choose the setup that best fits your workflow and environment.

Key Capabilities#

Your DGX Spark enables you to:

  • Run Inference: Deploy models for real-time AI applications

  • Develop AI Models: Train and fine-tune models with up to 200 billion parameters

  • Process Data: Handle large datasets with high-performance computing

  • Experiment Freely: Test new ideas without cloud computing costs

  • Scale Workloads: Connect multiple systems for larger projects

System Architecture#

The DGX Spark is built on NVIDIA’s Grace Blackwell architecture, providing:

  • Unified Memory: 128 GB of high-bandwidth memory for large models

  • High-Performance Computing: 20-core ARM64-based processor with integrated GPU

  • Advanced Connectivity: Wi-Fi 7, 10 GbE, CX7 NIC, and multiple I/O options

  • Compact Form Factor: 150mm x 150mm x 50.5mm desktop design

For detailed hardware specifications, see Hardware Overview.

Software#

Your system comes pre-configured with:

  • NVIDIA DGX OS: Optimized operating system for AI workloads

  • Development Tools: CUDA, cuDNN, and NVIDIA’s development ecosystem

  • Container Support: Docker and NVIDIA Container Runtime for easy deployment

  • NGC Integration: Access to NVIDIA’s container registry

For detailed software information, see Software.

Getting Started#

To begin using your DGX Spark:

  1. Initial Setup: Follow the Initial Setup - First Boot to configure your system

  2. Explore Examples: Try sample workloads to understand capabilities

  3. Configure Development Environment: Set up your preferred tools and frameworks

  4. Start Building: Begin your AI development projects

Note

For the most up-to-date tutorials and examples, visit https://build.nvidia.com/spark. This site is regularly updated with new content and serves as the primary resource for practical guides and use cases.