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Latency is crucial for AI inference because it directly impacts the speed at which a model can process inputs and generate outputs. In real-time applications—such as autonomous driving, video processing, or interactive voice assistants—users expect responses to be immediate. High latency can lead to poor user experience, reduced trust in the system, and even safety risks in critical domains.

Additionally, latency affects resource efficiency. Lower latency often means faster computation, which can be achieved through optimized model architectures, hardware acceleration (like GPUs or TPUs), or efficient inference techniques. Balancing accuracy and latency is a key challenge in deploying AI systems for real-world use.

Would you like me to explain how latency is measured and optimized in AI systems?