NVIDIA GTC 2026

Scaling the AI Factory for Global Capital Markets

March 16–19, 2026 San Jose, CA and Virtual

Join KX at NVIDIA GTC 2026 to discover how Tier 1 banks are moving from AI research to production-grade alpha generation with time-aware RAG.

Featured Session

From Signal to Strategy: Unlock Alpha With AI-Powered Research and Trading

Date: Monday, March 16
Time: 5:00 p.m. -05:40 p.m.

As markets move faster, the distance between signal and strategy matters more than ever. In this session, KX CEO Ashok Reddy shows how capital markets firms are deploying agentic AI architectures—powered by NVIDIA GPUs and the NVIDIA AI Factory stack, together with KX’s time-aware data layer—to combine real-time market data with unstructured intelligence and support alpha-driving decisions. He also explores why greater control over the intelligence layer is increasingly shaping long-term advantage in capital markets.

Key Takeaway: Attendees will receive a “take-home” AI factory blueprint and implementation checklist for scaling financial AI programs.

Ashok Reddy
Chief Executive Officer
KX

About the Speaker

Visit the KX booth #186 to experience the future of banking & trading

You can find us within the dedicated Financial Services (FSI) Pavilion on the main exhibition floor. We are hosting an experiential, demo-driven experience focused on the “AI Factory” for the modern enterprise.

Live Demos

See our AI Research Assistant powered by NVIDIA NIM, cuVS, and CUDA-X.

Expert Consultation

Meet with lead architects to discuss your specific GPU infrastructure and data fabric requirements.

Resource Hub

Pick up hard copies of our latest technical briefs and the 2026 Capital Markets Data Report.

Demo the world’s fastest database for vector, time-series, and real-time analytics

Start your journey to becoming an AI-first enterprise with 100x* more performant data and MLOps pipelines.

Process data at unmatched speed and scale
Build high-performance data-driven applications
Turbocharge analytics tools in the cloud, on premise, or at the edge

*Based on time-series queries running in real-world use cases on customer environments.

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