Unverified50% confidenceFactExact time
Inference is the process of responding to user requests after model training is complete; the computation per request is small but the request volume is enormous, placing more emphasis on throughput, latency, and unit cost.
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50%
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Long-term
Relevance
10/21/2026
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Related Claims
UnverifiedFor customer service, shopping guide, and notification-type reply models, what is needed is the behavioral pattern of directly giving the answer; once a large number of reasoning-on samples are mixed into the training data, the model will treat first reasoning at length and then answering as the task specification.79% similarUnverifiedLarge model inference refers to the process by which a model, after completing training, receives user input and generates output in real time, and is extremely sensitive to latency.79% similarUnverifiedThroughput refers to the number of requests a model can process per unit of time.79% similarVerifiedThe core idea of the test-time compute paradigm is to shift resources from the training phase to the answer generation process, trading more inference compute for higher-quality output.78% similarUnverifiedSpeculative execution refers to sending requests to both large and small models simultaneously, accepting the small model's result if quality meets the standard and discarding the large model's result; however, this introduces additional computational resource waste and is only worth considering in latency-extremely-sensitive scenarios.75% similar
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