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Operationalizing AI models at scale is a critical challenge for IT leaders. While the initial cost of training a large language model (LLM) can be significant, the real and often underestimated expense is tied to inference.AI inference—the process of using a trained model to generate an output—is the most resource-intensive and costly part of an AI application, especially because it happens constantly during production. Inefficient inference can compromise an AI project's potential return on investment (ROI) and negatively impact customer experience due to high latency.The full-stack appro

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Operationalizing AI models at scale is a critical challenge for IT leaders. While the initial cost of training a large language model (LLM) can be significant, the real and often underestimated expense is tied to inference.AI inference—the process of using a trained model to generate an output—is the most resource-intensive and costly part of an AI application, especially because it happens constantly during production. Inefficient inference can compromise an AI project's potential return on investment (ROI) and negatively impact customer experience due to high latency.The full-stack appro

via Red Hat Blog https://ift.tt/bH4UWCf

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