Nvidia GTC 2026: Agentic AI Takes Centre Stage With New LPU Chip

What happened

Nvidia's GTC 2026 conference in San Jose wrapped up this week, and by most accounts it delivered. CEO Jensen Huang's keynote featured two landmark hardware announcements that together suggest Nvidia is pivoting to meet the demands of the agentic AI era.

The headline reveal was the Language Processing Unit (LPU) -- a brand-new type of chip built on technology Nvidia acquired from chip startup Groq in December for $20 billion, its biggest purchase ever. Unlike Nvidia's GPUs, which have thousands of cores running operations in parallel, the Groq 3 LPU is built around a single core optimised for accelerating those GPUs during inference tasks.

The second announcement was a rack filled entirely with Nvidia's Vera CPUs -- the company's newest central processing units. This matters because agentic AI (AI that can take sequences of actions autonomously) requires far more data transfer and general-purpose compute than simple question-and-answer AI. CPUs handle that work, and Nvidia is treating them as the next bottleneck to solve.

Nvidia also announced NemoClaw, an enterprise version of the OpenClaw framework, signalling the company's ambition to own the full agentic AI stack from chips to software.

The conference also included Nvidia's reveal of open foundation models for robotics and physical AI under its Cosmos and Alpamayo lines, now available on GitHub and Foundry.

What this means for your business

Most business owners will never buy an Nvidia chip directly. But what happens at GTC shapes the tools you will use within 12 to 18 months.

The move towards agentic AI -- systems that can plan, act, and iterate without constant human input -- is the direction the whole industry is heading. If Nvidia is building specialised hardware for agents, the cloud platforms and SaaS tools built on top of that hardware will get faster and cheaper to run.

What this practically means: the AI tools you use today for simple tasks (drafting emails, summarising documents, answering questions) are about to become capable of running multi-step workflows autonomously. Processes that currently need a human in the loop -- chasing invoices, updating CRMs, screening applicants -- are the obvious targets.

The businesses that will benefit most are the ones that start getting familiar with AI workflows now, so they are ready when the underlying infrastructure catches up.