Unverified50% confidenceFactExact time
Token consumption and GPU time are two commonly confused units of measurement: the token count only counts the volume of text the model reads and writes, whereas the GPU time that truly determines cost also depends on model size, batching efficiency, memory bandwidth, and the degree of optimization of the inference framework.
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50%
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Long-term
Relevance
10/14/2026
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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%.71% similarUnverifiedGPU solutions use batching technology to combine multiple user requests for computation, which can greatly increase overall throughput and represents a trade-off between latency and throughput.71% similarUnverified缺少独立 GPU 的设备运行 2B 参数模型时生成速度可能低于每秒 5 个 token70% similarUnverified自回归推理是memory-bound（内存带宽受限）问题，因为batch size通常为1时算术强度极低，GPU大部分时间在等待数据搬运69% similarUnverifiedGPU global memory access latency can reach hundreds of clock cycles, while on-chip shared memory latency is only tens of cycles.68% similar
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