Insights Panel: Taming TCO and Scaling Inference for AI

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Insights Panel: Taming TCO and Scaling Inference for AI

OVERVIEW

A Three-Pillar Strategy to Taming TCO and Scaling Inference for AI

Enterprises often start their AI journey with public APIs, but quickly face new challenges at scale: unpredictable costs, latency for real-time apps, and the need for data compliance.

Join our panel of Red Hat AI experts as we break down the three-pillar strategy for building a high-performance, cost-controlled, and scalable inference platform on Red Hat AI. IT decision-makers will gain insightful guidance into taming TCO and becoming a strategic AI provider for their organization.

Attendees will gain a clear understanding of Red Hat’s end-to-end inference strategy, including:

The Engine (vLLM): How to maximize throughput and serve more users from your expensive GPU hardware.
The Scale (llm-d): The strategy for scaling massive models to thousands of users with Kubernetes-native distributed inference.
The Model Performance & Validation: How to slash hardware costs and de-risk your AI supply chain with compression, performance validation, and a curated model repository.

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