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AI inference is the production-time process in which a trained model receives new inputs and computes predictions, generated tokens, actions, or representations. This guide explains the mechanism, trade-offs, evaluation, and controls that matter in practice.
<p>For the last 24 months, one narrative justified every over-provisioned data center and bloated IT budget: the GPU scramble. Silicon was the new oil, and H100s traded like contraband. Reserve [...]
<p>The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use infe [...]
<p>Every GPU cluster has dead time. Training jobs finish, workloads shift and hardware sits dark while power and cooling costs keep running. For neocloud operators, those empty cycles are lost m [...]