The rise of autonomous, long-running AI agents has introduced a new class of compute demand, namely tasks that maintain large context windows, spawn concurrent subagents, and iterate continuously without cloud dependency. Security and privacy concerns are also accelerating the shift toward local agents.
Developers, by running autonomous agents on hardware they own with NVIDIA NemoClaw orchestrating execution, can keep sensitive context on-device, retain direct control over what an agent can access and eliminate per-token costs.
NVIDIA DGX Spark is designed to build and run autonomous agents locally. At Computex 2026, NVIDIA is making it significantly easier to get there, introducing a streamlined path from unboxing to running AI agents in minutes (excluding initial model download, which depends on network speed). There are also model performance improvements with Qwen3.6 and a guided multi-node cluster setup for teams that need to scale beyond a single device.
This post will cover what these updates mean for developers building agentic AI systems, including how to install NVIDIA NemoClaw, what it sets up, and how to build and run your first agent with OpenClaw on DGX Spark.
Prerequisites
