Red Hat AI Elevate New York City

Break

Accelerating Impact: Delivering Validated AI Solutions with Cisco and Gruve.Ai

Our partners will share real-world examples of leveraging Red Hat-optimized inference architectures to drive immediate, measurable results in the financial sector.

Optimizing Your AI Roadmap with Services & Training

Getting started with AI for most organizations is not about simply choosing an LLM and employing a chat bot. Actual real-world, business critical, implementations of AI can be overwhelming. The speed of adoption, the complexity of solutions, the changing landscape of guidelines, and more. In this session, we’ll explore how Red Hat’s approach to services - a framework consisting of 3 pillars: Consulting, Training, and Technical Account Management (TAM) - can help you navigate AI from identifying meaningful use cases and your team’s readiness, to getting those first AI workloads fully operationalized and supported.

Bridging the AI Value Gap: From Investment Anxiety to Operational Excellence

The "ROI Paradox" is real, but it's not a dead-end. The path to operational excellence begins by shifting focus from all-encompassing, high-risk AI transformations to precise, high-value workflow optimization. This session addresses the anxiety of investment and how the right strategy can support a practical, data-driven blueprint for a focused AI portfolio, ensuring your next dollar spent delivers sustainable, scalable returns.

Arrival, registration and breakfast

Maximizing GPU utilization in the enterprise

The most expensive GPU is the one that isn’t being used. While organizations are rushing to acquire compute power, many find their high-value hardware trapped in silos, leading to low utilization rates and stalled ROI. This session explores how Red Hat is helping leaders move from "dedicated hardware" to a shared, high-utilization GPU-as-a-Service model. We will discuss the orchestration strategies -- including fractional GPU sharing and intelligent scheduling -- that allow you to squeeze every ounce of performance out of your existing clusters, ensuring your AI infrastructure scales with your ambitions, not just your budget.

Why Your AI Strategy Starts and Ends with Inference

In the world of AI, model training is like building a car, but Inference is the act of driving it—the true moment of business value. Too many organizations focus on the "build" while neglecting the "run," leading to high costs and slow performance in production. This session demystifies Inference—the process of a trained model making real-time predictions—and explains why optimizing it is the most critical stage of the AI lifecycle for business success. We will demonstrate how Red Hat’s inference capabilities with vLLM at the core, are leveraged on the open hybrid cloud to not only reduce latency and operational costs but also to enable a seamless transition from experimental AI to essential, high-impact enterprise workflows. This includes a deep dive into real-world customer case studies that moved from "experimental" to "essential" by prioritizing their Red Hat-powered inference architecture.

Scaling Customer Success with Optimized AI

What does a "successful" AI implementation actually look like in a global bank or insurance firm? It looks like seamless customer experiences and reduced operational friction. This session explores the architecture of success in Financial Services. We will discuss how optimizing the AI lifecycle—specifically inference at scale—enables real-time decisioning in trading, lending, and customer service. Attendees will gain insight into the specific use cases that are currently driving the highest ROI in the sector and hear how industry leaders are overcoming the "high failure rate" statistics to build robust, compliant, and profitable AI ecosystems.

Keynote