Canonical is pleased to announce that  NVIDIA’s newly introduced NVIDIA Nemotron 3.5 Lightning, an open, customizable model built for always-on AI agents, is now available on Ubuntu with single-command installation. 

NVIDIA Nemotron 3.5 Lightning is an open 30B hybrid Mixture-of-Experts (MoE) model with 3B active parameters, designed for high-throughput agentic workloads. It is fully customizable, allowing organizations to own, fine-tune, and deploy the model wherever their agents operate while maintaining control over model behavior, data handling, and deployment.

Nemotron 3.5 Lightning available on Ubuntu at launch

Canonical is enabling access to Nemotron 3.5 Lightning at launch through inference snaps: pre-packaged AI inference runtimes distributed as snap packages for consistent deployment across systems. Developers and enterprises can deploy the model across supported NVIDIA platforms with a single command:

sudo snap install nemotron-3-5-lightning

Inference snaps transform Nemotron 3.5 Lightning deployment into a simple, standardized operation. This production-ready runtime scales consistently across workstations, edge devices, and servers, allowing teams to focus on building AI agents instead of managing infrastructure. Users deploying Nemotron 3.5 Lightning with inference snaps benefit from: 

• 1 million token context window: Nemotron’s context windows supports persistent, autonomous AI agents capable of maintaining context across long-running, multi-step workflows. 
• High-throughput hybrid MoE architecture: Nemotron 3.5 Lightning features a 30B Mixture-of-Experts model with 3B active parameters, distilled from NVIDIA’s frontier Nemotron 3 Ultra.
• Consistent runtime across environments: inference snaps ensure identical behavior on cloud, on-premises, and edge systems
• Simplified operations: standardized snap packaging reduces maintenance, patching, and upgrade complexity.
• Secure, confined execution: strict isolation with automatic updates and verified distribution

For enterprises, this translates into significantly faster deployment times, saving weeks of integration and validation work, and enabling scalable AI infrastructure with a single portable artifact. 

Learn more at: https://documentation.ubuntu.com/inference-snaps/