This ebook focuses on improving AI inference performance to reduce cost, latency, and infrastructure demands. It explains how large language models consume significant GPU memory during inference and outlines practical optimization strategies like quantization and sparsity. It also highlights the importance of choosing the right runtime, particularly vLLM, to improve batching and memory efficiency. Red Hat AI brings these elements together with validated models, an optimized inference server, and compression tools that help organizations deploy scalable, high-performing AI workloads across hybrid cloud environments.

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