NVIDIA GTC 2026: Everything That Matters for AI Businesses

Model Intelligence & News

19 March 2026 | By Ashley Marshall

NVIDIA’s GTC conference in San Jose this week has delivered what Jensen Huang called “the beginning of a new renaissance in software.” That is a bold claim, even by NVIDIA standards. But after working through the announcements, the substance largely matches the ambition.

NemoClaw: NVIDIA Goes All In on OpenClaw

The single most significant announcement for the agentic AI community is NemoClaw. NVIDIA has built a complete stack for the OpenClaw agent platform that installs with a single command, bringing together NVIDIA’s Nemotron models and the newly announced OpenShell runtime.

Jensen Huang’s framing was striking: “Mac and Windows are the operating systems for the personal computer. OpenClaw is the operating system for personal AI.” That positioning places OpenClaw alongside the most important software platforms in computing history.

What NemoClaw actually delivers:

• One-command install of Nemotron models plus OpenShell runtime on the OpenClaw platform
• Privacy and security controls that make autonomous agents (claws) more trustworthy
• Privacy router that lets agents use frontier models in the cloud while keeping sensitive processing local
• Runs on dedicated hardware from GeForce RTX PCs and laptops to DGX Station and DGX Spark

For businesses already running OpenClaw (like us at Precise Impact), this is a direct upgrade path. NemoClaw adds the security and governance layer that enterprise deployments need: policy-based controls, network guardrails, and privacy boundaries that let agents operate autonomously within defined limits.

The hybrid model approach is particularly smart. Agents can tap local Nemotron models for privacy-sensitive work while routing to frontier cloud models when they need maximum capability. This mirrors the architecture we already recommend to clients, now with first-class NVIDIA support.

OpenShell: The Security Layer Agents Have Been Missing

OpenShell deserves its own section because it solves a problem every business deploying AI agents faces: how do you give an autonomous agent enough access to be productive while preventing it from doing things it should not?

OpenShell is an open source runtime that provides an isolated sandbox with:

• Policy-based security that defines what agents can and cannot do
• Network guardrails that control what external systems agents can access
• Privacy controls that keep sensitive data within defined boundaries
• Compatibility with major security platforms including Cisco AI Defense, CrowdStrike Falcon, Google, and Microsoft Security

The enterprise partnerships here are telling. When Cisco, CrowdStrike, and Microsoft are building compatibility with your agent security runtime, it signals that the industry takes this seriously. Agent security is no longer an afterthought; it is becoming a first-class infrastructure concern.

For businesses evaluating agentic AI deployment, OpenShell changes the conversation from “is it safe enough?” to “which security policies do we need to configure?”

Vera CPU: Hardware Purpose-Built for the Agentic Era

NVIDIA’s Vera CPU is the first processor explicitly designed for agentic AI workloads. The headline numbers are impressive: twice the efficiency and 50% faster than traditional rack-scale CPUs. But the architectural decisions behind those numbers matter more than the benchmarks.

Why a dedicated CPU for agentic AI? Because agentic workloads are fundamentally different from traditional compute. When AI agents run tools, execute code, interact with data, and validate their own results, the bottleneck shifts from the GPU to the CPU infrastructure that orchestrates these operations.

Key specifications:

• 88 custom Olympus cores with NVIDIA Spatial Multithreading
• LPDDR5X memory delivering up to 1.2 TB/s bandwidth at half the power of conventional CPUs
• NVLink-C2C interconnect with 1.8 TB/s coherent bandwidth (7x PCIe Gen 6)
• 256-CPU rack configuration sustaining 22,500+ concurrent CPU environments

The customer list speaks volumes: Alibaba Cloud, ByteDance, Meta, Oracle, CoreWeave, Lambda, and every major system builder from Dell to Supermicro. Cursor, the AI coding tool, is adopting Vera to boost performance for its coding agents.

For most businesses, Vera is not a direct purchase. But it matters because the cloud providers and AI platforms you use will be running Vera within months, which means your agentic workloads will run faster and cheaper without you changing anything.

Vera Rubin: The AI Factory Platform

The Vera Rubin platform is NVIDIA’s complete AI factory vision brought to production: seven new chips working together as one coherent system. The components:

• Vera Rubin NVL72 GPU racks with 72 Rubin GPUs and 36 Vera CPUs
• Vera CPU racks with 256 CPUs for agentic inference
• Groq 3 LPX inference accelerator racks for low-latency agentic inference
• BlueField-4 STX storage racks for AI-native context memory
• Spectrum-6 SPX Ethernet racks for high-speed networking

The performance claims are staggering: training large mixture-of-experts models with one-quarter the GPUs compared to Blackwell, and up to 10x higher inference throughput per watt at one-tenth the cost per token.

Both Anthropic and OpenAI issued statements of support. Dario Amodei noted that “NVIDIA’s Vera Rubin platform gives us the compute, networking and system design to keep delivering.” Sam Altman said it will let OpenAI “run more powerful models and agents at massive scale.”

The Groq 3 LPX integration is particularly interesting for agentic use cases. Designed for low-latency, large-context demands, it delivers up to 35x higher inference throughput per megawatt compared to current solutions. When your agents need to reason across million-token contexts in real time, latency matters enormously.

NVIDIA Agent Toolkit: The Enterprise Ecosystem

The Agent Toolkit announcement is less flashy but arguably more important for businesses. NVIDIA is providing the open source models, blueprints, and tools that make enterprise agent deployment practical.

The enterprise partnerships list reads like a who’s who of business software:

• Adobe: Running creativity and marketing agents on Agent Toolkit
• Atlassian: Evolving Rovo AI agents for Jira and Confluence
• Salesforce: Powering Agentforce with Nemotron models, using Slack as the orchestration layer
• SAP: Enabling AI agents through Joule Studio
• ServiceNow, Cisco, CrowdStrike, Red Hat: Building agent infrastructure

The AI-Q blueprint is worth noting. It uses a hybrid architecture with frontier models for orchestration and Nemotron open models for research, cutting query costs by more than 50% while topping the DeepResearch Bench accuracy leaderboards. That is the kind of practical cost-performance optimisation that businesses need.

What This Means for Your AI Strategy

Here is the practical takeaway from GTC 2026 for businesses at any stage of AI adoption:

If you are already running agentic workflows

NemoClaw and OpenShell give you a direct upgrade path for security and governance. If you are using OpenClaw (as we are at Precise Impact), the NemoClaw stack adds enterprise-grade guardrails without changing your existing workflows. This is the answer to “how do we make our agents production-ready?”

If you are planning AI infrastructure

The Vera CPU and Vera Rubin platform signal where the industry is heading. Cloud providers will adopt this hardware aggressively, which means agentic workloads will become cheaper and faster to run over the next 12 months. Factor this into your cost projections.

If you are evaluating AI adoption

The enterprise software partnerships (Salesforce, SAP, Atlassian, Adobe) mean that agentic AI is coming to the tools you already use. The question is not whether to adopt it but how to prepare your organisation for the capability when it arrives in your existing platforms.

If you are concerned about security and governance

OpenShell and the security partnerships (Cisco, CrowdStrike, Microsoft) represent the industry maturing around agent safety. This is no longer experimental technology with no guardrails. Policy-based security for autonomous agents is now a product category with major vendors competing to provide it.

The Bigger Picture

GTC 2026 marks the moment that agentic AI moved from “interesting technology” to “infrastructure investment.” When NVIDIA builds dedicated CPUs for agent workloads, when every major enterprise software vendor announces agent partnerships, and when the security industry mobilises around agent governance, the signal is clear.

The businesses that move now, building their agentic capabilities, establishing governance frameworks, and training their teams, will have a significant advantage over those that wait for the next conference cycle. The infrastructure is being built. The tools are available. The question for every business leader is: what are you going to build with them?

Frequently Asked Questions

Frequently Asked Questions

What is NemoClaw and how does it relate to OpenClaw?

NemoClaw is NVIDIA’s stack built specifically for the OpenClaw agent platform. It installs NVIDIA’s Nemotron models and the OpenShell secure runtime in a single command, adding enterprise-grade privacy, security, and governance controls to OpenClaw’s existing agent capabilities. Think of it as an upgrade package that makes OpenClaw agents more trustworthy for business use.

Do I need NVIDIA hardware to benefit from these announcements?

Not directly. NemoClaw runs on a range of hardware including GeForce RTX PCs, and the Vera CPU will be deployed by cloud providers (AWS, Azure, Oracle, CoreWeave) so you will benefit through lower costs and better performance on your existing cloud platforms. The enterprise software integrations (Salesforce, SAP, Atlassian) will bring agentic capabilities to tools you may already use without any hardware purchase.

What should my business do right now based on GTC 2026?

Three actions: first, evaluate OpenClaw and NemoClaw if you are not already running agentic workflows, as the barrier to entry has dropped significantly. Second, review your AI governance framework with OpenShell’s capabilities in mind, because policy-based agent security is now available as standard infrastructure. Third, factor the coming cost reductions from Vera and Vera Rubin into your AI budget planning for the next 12-18 months.