Unverified50% confidenceFactExact time
The output token unit price of large model APIs is typically 2–4 times that of input tokens, because the autoregressive generation mechanism requires one independent forward pass per output token and KV Cache occupies a large amount of GPU memory.
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50%
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Long-term
Relevance
9/11/2026
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Related Claims
UnverifiedThe core bottleneck in large model inference on GPUs is memory bandwidth; autoregressive generation of each token requires loading the full KV Cache from HBM, with compute utilization typically below 5%.75% similarUnverifiedIn large model API call scenarios, cache hit rate refers to the reuse ratio of the KV Cache (key-value cache), where the model can directly read the attention key-value pairs of already-processed tokens from the cache.74% similarUnverifiedLarge model APIs are typically billed by token count, and generation speed is measured in tokens per second.71% similarUnverifiedWhen model weights and KV Cache cannot be fully loaded into GPU VRAM and some data must be offloaded to main memory, inference speed drops by one to two orders of magnitude.71% similarUnverifiedThe KV Cache (Key-Value Cache) strategy stores already-computed attention key-value pairs in GPU memory for reuse, which consumes a large amount of GPU memory resources.69% similar
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