# DigitalOcean and NVIDIA Discuss Open-Source AI and Agentic AI Development at Deploy 2026

DevFeed: [DigitalOcean and NVIDIA Discuss Open-Source AI and Agentic AI Development at Deploy 2026](<https://devfeed.tech/articles/open-by-design-how-nvidia-and-digitalocean-are-building-the-stack-for-the-always-on-agentic-era-19925.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/open-by-design-tech>)

Author: Jess Lulka

Published: 2026-06-02T18:29:57Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Digital Ocean](<https://devfeed.tech/topics/digital-ocean.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Development](<https://devfeed.tech/topics/development.md>), [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [community](<https://devfeed.tech/tags/community.md>), [design](<https://devfeed.tech/tags/design.md>), [developers](<https://devfeed.tech/tags/developers.md>), [digitalocean](<https://devfeed.tech/tags/digitalocean.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [llms](<https://devfeed.tech/tags/llms.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>)

## AI overview

The article covers a DigitalOcean Deploy 2026 session about open-source AI, agentic AI development, and the infrastructure and model support needed to move open models into production. It discusses NVIDIA Nemotron, software libraries, and running open-weight large language models locally for performance, privacy, and customization.

## Source excerpt

The growth of generative AI isn't driven solely by AI companies with proprietary models. Open-source AI is reshaping the developer ecosystem, fueled by a growing community of builders. But what does it take to go from open models to production-ready agentic AI, and what do developers need to know to get there? This question was the focus of the DigitalOcean Deploy session, "Open by Design: How NVIDIA and DigitalOcean Are Building the Stack for the Always-On Agentic Era." During this 30-minute chat, Kari Briski, VP Gen AI at NVIDIA, and Salman Paracha, SVP AI at DigitalOcean, discuss why AI-native teams are demanding openness, model flexibility, and infrastructure built for agents that never sleep--and what NVIDIA and DigitalOcean are doing to build support for this next generation of AI development. Watch the full recorded session from Deploy 2026: View YouTube video Open-Source Models Need Commitment, Not Just a Launch There are many open models in the ecosystem, but having great models doesn't guarantee they will be consistently improved or regularly updated. NVIDIA noticed a potential gap in this space for its enterprise customers, who regularly wanted access to open-source models that are launched and then left untouched. This spurred the development of open models such as NVIDIA Nemotron. Released in March 2026, it serves as a family of multi-modal models designed for agentic AI. Having access to these open models enables developers to create agentic applications that require advanced reasoning, high compute efficiency, and open source standards. With Nemotron models and NVIDIA software libraries, developers can evolve their projects over time and receive regular updates and expanded support. Running open-weight LLMs locally gives you more control over performance, privacy, and customization. This NVIDIA Nemotron 3 tutorial walks through deploying NVIDIA's Nemotron 3 Nano on a DigitalOcean GPU Droplet, helping you experiment with efficient open models on dedicat