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World Congress 2026 Europe • Jul 9, 2026 • Session details

Nemotron: NVIDIA's open model strategy for developers

Sergio Perez

NVIDIA isn't just open-sourcing model weights—they're releasing the entire agentic AI playbook. Learn how the Nemotron stack enables developers to build, test, and scale autonomous applications.

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#1 about 3 min

NVIDIA's strategy for open-source foundation models

NVIDIA comprehensively contributes to open source algorithms by sharing foundational models, datasets, and training libraries for developers.

#2 about 3 min

The evolution from standard chatbots to agentic systems

Artificial intelligence has shifted from merely responding to questions toward executing long-running operations and adapting tasks on your behalf.

#3 about 3 min

Understanding the components of an agentic harness

Large language models require a surrounding framework comprising tool calls, specialized execution environments, memory, and governance to function autonomously.

#4 about 2 min

Core requirements for state-of-the-art agentic language models

Effective agentic systems rely on a powerful central model, specialized smaller models, and robust test-time scaling for extended context windows.

#5 about 4 min

The nemotron model family and open-source assets

A varied array of model weights, ranging from 30 billion to 550 billion parameters, comes coupled with training libraries and multimodal assets.

#6 about 2 min

Evaluating applications with synthetic data and nemotron personas

Engineering localized generative applications requires testing against unique regional populations using specialized synthetic personas and sovereign alignment datasets.

#7 about 4 min

Training and evaluating models with open-source repositories and gyms

Developers can fine-tune complex architectures like mixture-of-experts pipelines and rigorously evaluate their outputs on specific tasks using tailored logic gyms.

#8 about 7 min

Deep dive into nemotron ultra architecture and performance

A sophisticated sparse architecture layered with distinct training routines pushes ultra parameters to achieve exceptional benchmark results with fast inference.

#9 about 3 min

Deploying on edge devices and contributing to upstream repositories

Lightweight model variants support localized edge hardware, and teams are strongly encouraged to bring resulting finetunes or library expansions back to the community.

Matching moments

2:14 min

Building and fine-tuning models with the NeMo framework

Anshul Jindal Anshul Jindal +1 · WWC Europe 2026

2:15 min

Open-source community and machine learning frameworks

Gian Marco Iodice Gian Marco Iodice · WWC 2025

1:32 min

Architectural patterns for developing robust generative AI applications

Julián Duque Julián Duque · WWC 2025

5:01 min

Leveraging large language models for code optimization and development

Stephan Gillich Stephan Gillich +3 · WWC 2024

2:24 min

Advancing open source foundation and physical models

Julia Koch Julia Koch +1 · WWC Europe 2026

3:22 min

Evaluating advanced artificial intelligence platforms for daily recruitment

Rudi Bauer Rudi Bauer +1 · Cappuccino with HR

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