Unverified50% confidenceFactExact time
Large-model inference continuously consumes GPU compute; if revenue per call cannot cover marginal compute cost, scaling up will instead amplify losses.
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50%
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Long-term
Relevance
10/31/2026
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UnverifiedThe gains from speculative decoding are diluted in large-batch scenarios, as GPU compute capacity tends to be saturated in such cases.80% similarVerifiedThe bottleneck in large model inference is often not compute but memory bandwidth, and GPU compute utilization during the inference phase is often less than 10% of peak.78% similarUnverifiedGPU显存无法超卖，且一旦显存溢出会导致OOM直接崩溃，大模型推理具有强计算密集性78% similarVerifiedThe computational consumption during large model inference is the root cause of persistently high API costs, as each request requires a large number of floating-point operations on a GPU cluster and cannot have its cost amortized by training once and reusing repeatedly.76% similarUnverified推理时计算的扩展虽能提升单次输出质量，但成本以超线性方式增长，大规模部署往往难以经济可行75% similar
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