FOD#144: New Scaling Law? What “Agentic Scaling" Is – Inside NVIDIA’s Biggest Idea at GTC 2026

2026-03-16 · Source: Turing Post · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

NVIDIA's GTC 2026 conference highlighted the company's expanding role across the entire AI infrastructure stack, moving beyond just GPUs to a vertically integrated computing company with open horizontal integration. A key announcement was the introduction of "agentic scaling," a new scaling law for AI systems that call tools, write code, search, and interact with other AIs, demanding different infrastructure pressures focused on latency and memory movement. NVIDIA unveiled NemoClaw, a framework for autonomous agents, and new hardware like the Vera Rubin platform and GPU + LPU rack, designed to optimize for agentic workloads. The company is also fostering an open ecosystem with partners like Cursor and LangChain for collaborative model development and extending its reach into Physical AI, robotics, autonomous vehicles with models like Alpamayo, and even space-based AI infrastructure.

Key takeaway

For AI Architects and MLOps Engineers designing next-generation AI systems, you should recognize NVIDIA's shift towards a heterogeneous, full-stack AI factory. This means evaluating infrastructure not just on GPU performance, but on its ability to support agentic scaling, low-latency inference, and multimodal processing across diverse environments, from data centers to robotics and space. Your future deployments will benefit from specialized hardware and software layers working in concert.

Key insights

NVIDIA is redefining AI infrastructure for agentic workloads, integrating specialized hardware and software across the entire AI stack.

Principles

• AI infrastructure is shifting towards heterogeneity.
• Agentic systems require specialized hardware optimization.
• Open ecosystems can accelerate foundation model development.

Method

NVIDIA proposes a "theory of AI infrastructure" that stitches together energy, silicon, networking, storage, models, software, robots, telecom, and data centers into one production system, with specialized racks for different AI phases.

In practice

• Utilize NVIDIA's NemoClaw for autonomous agent development.
• Consider Vera Rubin platform for agentic workload optimization.
• Explore Gemini Embedding 2 for multimodal data processing.

Topics

• NVIDIA Strategy
• Agentic AI
• AI Infrastructure
• Open AI Ecosystem
• Physical AI

Code references

• MoonshotAI/Attention-Residuals

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Editorial summary, takeaway, and curation by AIssential. Original article published by Turing Post.