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NVIDIA GTC is a wrap! Here's your recap from WWT
NVIDIA GTC 2026 was packed with announcements, conversations and a clear sense that enterprise AI has hit an inflection point. Whether you joined us at booth #1821, caught a session or watched the keynote at Mezcal, we are glad you were part of it — and we have plenty to share from the week.
The Inference Age Has Arrived and More: What WWT Took Away from NVIDIA GTC 2026
World Wide Technology Awarded NVIDIA's 2026 NPN AI Excellence Partner of the Year, NPN AI Enterprise Software Partner of the Year and NPN Federal Partner of the Year
WWT's thought leadership sessions at NVIDIA GTC 2026:
Accelerating Innovation with AI Studio and Foundry: Cases Driving Customer Success
Presenter: Nathan McKie, Sr. Executive AI Advisor
Discover how organizations are accelerating their AI journeys with WWT's AI Studio and Foundry—leveraging NVIDIA NIM, NVIDIA NeMo and NVIDIA AI Blueprints to move quickly from ideas to validated prototypes and scalable, production-ready solutions. We will share real-world examples of how this approach is delivering meaningful business outcomes in industries such as global service providers and energy production.
Close the Sim-to-Real Gap: Next-Gen Techniques for Synthetic Data Mastery
Presenter: Paige Reiter, Technical Solutions Architect
Stop relying on limited real-world datasets. Join us to explore proven techniques to leverage NVIDIA Omniverse Replicator and NVIDIA Cosmos to create synthetic imagery for building robust computer vision models that recognize rare objects and challenging conditions. We will dive deep into synthetic imagery best practices, the NVIDIA hardware and software stacks that accelerate the process, and the critical decision points that maximize your synthetic data ROI and accelerate your path to production-ready Physical AI.
Dive into AI technologies with our demos
WWT AI Network Assistant: This is an AI-powered tool that automates network troubleshooting, configuration validation and compliance checks for enterprise IT environments. It translates natural language queries into CLI commands, executes them on network devices via SSH, analyzes outputs using AI and provides actionable insights. Additionally, it integrates with Cisco documentation for reference and supports automated runbook execution to streamline network operations.
Daily Ops Summary Agent: This agent uses AI-powered summarization to bring the most relevant information from Incident and Change management into an intelligent report for operation specialists. It uncovers insights and trends buried in service management data, giving operations staff clearer visibility into performance, patterns, and potential improvements.
Incident Knowledge Assistant: This assistant provides fast, targeted guidance for IT Operations specialists. It uses representative data from an IT Service Management Platform to identify the most relevant information from Incident, Change, and Problem Management, as well as the Knowledge Base. This helps IT teams quickly triage and resolve trouble tickets, improving efficiency and reducing downtime.
Intelligent Resource Optimizer: This AI agent is designed to proactively manage and optimize computing infrastructure. It moves beyond static analysis to provide proactive infrastructure management. A specialized Time Series model is used to analyze historical system utilization and accurately forecast future resource demands. This enables IT Operations teams to receive data-driven recommendations for scaling and optimization of computing equipment, ensuring peak performance and cost-efficiency across the environment.
The RAG based digital human will be able to interact with users to answer questions on the hydrated data set. This data can be dynamically changed to create a fresh experience. This version of the Digital Human is launchable in our AIPG and can be interacted with during in-person tours.
LLM Clinical Trial Matcher: The recruitment of patients for clinical trials presents significant challenges for pharmaceutical companies, often delaying drug development, increasing cost and leading to study failure. This demo explores how AI applied to anonymized health records enables efficient trial matching —at single, bulk and multi-trial levels — reducing time, improving accuracy, and enhancing access for researchers, providers and patients.
AI Data Flywheel on Cisco Secure AI Factory with NVIDIA: The NVIDIA Data Flywheel Blueprint is designed to continuously improve AI models using real-world data. It starts with a large, high-accuracy model running in a central environment, captures usage data and difficult examples, retrains and fine-tunes the model, and then produces smaller, optimized models that can be deployed closer to where work happens. WWT runs this core process on the Cisco Secure AI Factory with NVIDIA infrastructure and then generates right-sized models for edge environments. This reduces latency and hardware requirements while maintaining model accuracy.
Pre-Visit Planning Tool: The collaboration culminated in the Pre-Visit Planning Tool (PVPT), a demo designed to show how AI and hardware acceleration can work together to simplify the patient review process. The tool curates imaging, pathology and treatment data into a unified longitudinal summary, helping clinicians see the whole story of a patient's care in seconds rather than minutes.
WWT Sets a New Standard for Enterprise-Ready AI with Cisco Secure AI Factory with NVIDIA
WWT's AI Proving Ground Podcast
This year, the AI Proving Ground Podcast recorded live from the NVIDIA GTC expo floor. Our host Brian Feldt sat down with our experts and partners, including Cisco, Everpure, NetApp and Schneider, to capture real conversations happening in the middle of one of the biggest AI events of the year.
What We Learned at NVIDIA GTC 2026
Explore WWT's AI Proving Ground
AI Proving Ground
Thanks to our supporting partners
We'd like to recognize our partners for making our presence at NVIDIA GTC possible. Thank you for the partnership!
Read the latest WWT Research in AI and Data
Every Manufacturer Wants to Be AI-first. Few Know Where to Start
AI at Scale on Kubernetes: Why Platform Discipline Determines Accelerator Efficiency
AI Coding Assistants: Enterprise Market Landscape and Tools Evaluation
The Reality of AI in State Government and Why AI Centers of Excellence Matter
Automation Priorities for 2026
Data Maturity Model
Executive Insight: Lessons in Power, Placement and the Path to AI at Scale
Executive Insight: The Thing About Bubbles and AI