Throughput denotes the number of input samples or tasks that a model can process within a specific time frame. It is a measure of the system's capacity and efficiency in handling multiple requests.

Typically, machine learning researchers refer to throughput as being either high or low:

High throughput: 

Advantages: High throughput is beneficial when speed, real-time processing, and scalability are critical. It allows AI systems to handle a large number of tasks or data points quickly and efficiently.

Use cases: High throughput is favored in applications such as autonomous vehicles, real-time financial trading, customer support chatbots, content delivery networks, and situations where rapid decision-making is crucial.

Low throughput:

Advantages: Low throughput might be acceptable or even preferable as it may allow for deeper analysis, more complex computations, and a focus on accuracy over speed.

Use cases: Low throughput can be suitable for tasks such as scientific simulations, complex data analysis, research experiments, and applications where precision and thoroughness are prioritized over immediate response times.

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