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Category: Hardware & Infrastructure
Category: Hardware & Infrastructure
Latency in AI systems is the time delay between sending a request to a model and receiving the response, critical for real-time applications.
Latency includes model loading time, data preprocessing, inference computation, and result postprocessing. Each component must be optimized for low-latency applications.
Techniques like model quantization, caching, and edge deployment reduce latency from seconds to milliseconds.
Low latency enables real-time AI applications like autonomous driving, voice assistants, and live translation. User experience degrades rapidly with increased latency.
The difference between 100ms and 1s latency can determine whether an AI application is viable for production use.
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