Uploaded April 2026 | Updated September 2026, 2 weeks ago
Build an always-on, fully local AI assistant on DGX Spark. This step-by-step tutorial walks through setting up a more secure, long-running AI agent using NVIDIA NemoClaw with OpenClaw for terminal interaction. It runs Nemotron 3 Super locally on DGX Spark and supports multiple interfaces and automated workflows.
The result is a fully local assistant that can execute tasks, access tools, and run continuously while keeping all data and models on your own infrastructure.
What you’ll build:
Interfaces: Terminal, web UI, and Telegram
Agent capabilities: Web research, workspace management, and task scheduling
Sandboxed runtime: More secure execution with NemoClaw and NVIDIA container runtime
Local model serving: Ollama-based deployment with no external dependencies
📝 Tech blog: developer.nvidia.com/blog/build-a-secure-always-on-local-ai-agent-with-nvidia-nemoclaw-and-openclaw
🛠️NemoClaw on GitHub: github.com/NVIDIA/NemoClaw
📑 Playbook: build.nvidia.com/spark/nemoclaw
🧠 Join the community on Discord: discord.com/channels/1019361803752456192/1482072289511211200
0:00 - Introduction to the Local AI Assistant Stack
0:21 - Interacting with the Assistant via Terminal UI
1:43 - Using the Web UI for Assistant Management
2:12 - Messaging the Agent Through Telegram
3:03 - Initial Installation and Docker Configuration
3:55 - Setting Up Ollama and Nemotron 3 Super
5:16 - NemoClaw Installation and Configuration Wizard
6:52 - Integrating Telegram, Discord, Slack
8:09 - Finalizing the Sandbox and Accessing the Dashboard
10:19 - Port Forwarding for Remote Web UI Access
12:14 - Pairing and Approving Telegram Communications
Build an always-on, fully local AI assistant on DGX Spark. This step-by-step tutorial walks through setting up a more secure, long-running AI agent using NVIDIA NemoClaw with OpenClaw for terminal interaction. It runs Nemotron 3 Super locally on DGX Spark and supports multiple interfaces and automated workflows.
The result is a fully local assistant that can execute tasks, access tools, and run continuously while keeping all data and models on your own infrastructure.
What you’ll build:
Interfaces: Terminal, web UI, and Telegram
Agent capabilities: Web research, workspace management, and task scheduling
Sandboxed runtime: More secure execution with NemoClaw and NVIDIA container runtime
Local model serving: Ollama-based deployment with no external dependencies
📝 Tech blog: developer.nvidia.com/blog/build-a-secure-always-on-local-ai-agent-with-nvidia-nemoclaw-and-openclaw
🛠️NemoClaw on GitHub: github.com/NVIDIA/NemoClaw
📑 Playbook: build.nvidia.com/spark/nemoclaw
🧠 Join the community on Discord: discord.com/channels/1019361803752456192/1482072289511211200
0:00 - Introduction to the Local AI Assistant Stack
0:21 - Interacting with the Assistant via Terminal UI
1:43 - Using the Web UI for Assistant Management
2:12 - Messaging the Agent Through Telegram
3:03 - Initial Installation and Docker Configuration
3:55 - Setting Up Ollama and Nemotron 3 Super
5:16 - NemoClaw Installation and Configuration Wizard
6:52 - Integrating Telegram, Discord, Slack
8:09 - Finalizing the Sandbox and Accessing the Dashboard
10:19 - Port Forwarding for Remote Web UI Access
12:14 - Pairing and Approving Telegram Communications
