As generative AI systems move from experimentation into production, inference becomes the dominant workload. For most organizations, the central challenge is no longer model accuracy alone, but how to deliver large language model responses at scale while maintaining predictable latency, sustainable infrastructure costs, and operational stability.

This whitepaper is intended for enterprise architects, platform engineers, and technical decision-makers evaluating inference infrastructure options. It addresses a practical question: how does Intel Gaudi 3 perform for real-world inference workloads when deployed on Red Hat OpenShift AI, and what does that performance mean for cost, capacity planning, and production readiness?

To answer this, AHEAD partnered with Intel to conduct structured, repeatable benchmarking of large language model inference on Gaudi 3 accelerators within an OpenShift AI environment. The goal was not to produce a single peak performance number, but to evaluate how throughput, latency, and concurrency behave under realistic conditions, including varying prompt sizes and increasing concurrent request volumes.

Download the full paper to learn more.