Unverified50% confidenceFactExact time
每个中间推理token与输出token消耗等量的GPU算力，Extra High档位单次请求FLOPs可能是普通生成模式的数十倍
1
Sources
50%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
UnverifiedModels with larger parameter counts and more reasoning steps may consume 10–50 times the GPU compute of lightweight models.74% similarUnverifiedContinuous batching immediately removes completed sequences and inserts new requests after each decoding step, raising GPU utilization from 30–50% in traditional approaches to over 80%.73% similarUnverifiedThe GPU resources required for a single inference of a hundred-billion-parameter flagship model may be 10 to 20 times higher than those of a ten-billion-parameter model.73% similarUnverifiedOutput tokens are typically more expensive than input tokens because outputs require autoregressive token-by-token generation, which has higher computational intensity and lower GPU utilization.69% similarUnverified服务同等数量的并发用户，超大模型所需的GPU集群规模可能是同等智能水平小模型的5至10倍69% similar
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