Even the chip makers are making LLMs

2026-03-10 · Source: Stack Overflow Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

NVIDIA, a prominent chip manufacturer, has significantly expanded its role into large language model (LLM) development, driven by an "extreme co-design" philosophy that tightly integrates hardware and software. This approach allows NVIDIA to optimize its GPUs, like the Blackwell with NVFP4 precision, by developing models such as the open-source Nemotron family. The Nemotron models, including Nano, Super, and Ultra, are fully open-source, providing open weights, training data, and recipes to foster specialization and rapid iteration within the AI community. This strategy addresses enterprise concerns about data liability and auditability, enabling partners like ServiceNow to build domain-specific models. NVIDIA views these models as evolving software libraries, with a roadmap that includes regular updates and a future capability for external contributions to model architecture.

Key takeaway

For AI Engineers and CTOs evaluating LLM adoption, NVIDIA's fully open-source Nemotron models offer a transparent and auditable foundation. This approach, coupled with NVIDIA's hardware-software co-design, provides a robust platform for developing specialized AI agents, mitigating data liability concerns, and optimizing performance. You should investigate Nemotron for your next project, especially if domain-specific customization and verifiable training data are critical requirements.

Key insights

NVIDIA's co-design of hardware and fully open-source LLMs drives mutual optimization and fosters broad AI development.

Principles

• Hardware and software co-design is critical for AI acceleration.
• Open-source models, data, and recipes accelerate innovation.
• Reduced precision training improves memory and performance.

Method

NVIDIA employs an "extreme co-design" feedback loop between model builders and hardware architects to optimize GPU performance and model efficiency, including training in reduced precision (e.g., NVFP4) and developing disaggregated serving frameworks.

In practice

• Explore Nemotron models for domain-specific AI agent development.
• Utilize NVIDIA's open training data to build trusted, auditable models.
• Consider reduced precision training for memory and performance gains.

Topics

• NVIDIA
• Large Language Models
• Hardware-Software Co-design
• Model Training Precision
• Open-source AI Models

Best for: AI Engineer, CTO, VP of Engineering/Data, Machine Learning Engineer, AI Architect, AI Product Manager

Related on AIssential

• NVIDIA Unveils New Open Models, Data and Tools to Advance AI Across Every Industry — NVIDIA's open-source releases aim to accelerate AI development through synthetic data generation and optimized deployment tools.
• AWS and NVIDIA deepen strategic collaboration to accelerate AI from pilot to production — AWS and NVIDIA are deepening their partnership to provide integrated, scalable, and secure AI infrastructure and services.
• Nvidia intros six new AI chips and new open models — Nvidia's new Rubin platform and specialized open models emphasize a full-stack AI factory approach beyond just GPUs.
• Open-Source AI Tool Upgrades Speed Up LLM and Diffusion Models on NVIDIA RTX PCs — NVIDIA's CES 2026 announcements significantly boost AI PC developer capabilities through framework optimizations and new model releases.
• Why Nvidia builds open models with Bryan Catanzaro — NVIDIA's Nemotron project strategically advances AI infrastructure through open models, data, and research, driven by both internal product needs and ecosystem growth.
• 😺 🎙️ Watch: NVIDIA's 120B Model Runs Like a 12B. Here's How. — NVIDIA's Nemotron-3 Super enables large AI models to run efficiently on consumer GPUs via a mixture of experts.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Stack Overflow Blog.