Unverified50% confidenceFactExact time
For large models, "inference efficiency" usually refers to the number of tokens that can be generated per unit time (throughput) and the first-token latency.
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50%
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Long-term
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10/19/2026
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UnverifiedContext length determines the maximum number of tokens a model can process in a single pass and directly affects VRAM usage.79% similarUnverifiedWhen evaluating an inference engine, throughput and latency must be distinguished: throughput usually refers to the total number of tokens generated by the whole system per unit of time, while latency focuses on the response speed of a single request, divided into first-token latency and per-token generation interval; a larger batch can improve throughput but may raise the latency of a single request.79% similarUnverified大模型每个数字都是逐 token 生成的，而非逐位计算，导致其算术能力在小数、除法、多步混合运算上不稳定77% similarUnverifiedThe computational load of constraint validation is proportional to vocabulary size and must be repeated for every generated token, making performance optimization critical.76% similarUnverifiedBy eliminating the token-by-token generation loop, the time complexity of a single inference is reduced from O(n) to approximately constant, giving a clear throughput advantage in high-concurrency routing scenarios74% similar
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