Unverified70% confidenceFactExact time
本地部署MoE模型可将边际推理成本压缩至接近电费的水平，相比云端API按Token计费模式大幅降低成本
1
Sources
70%
Confidence
Long-term
Relevance
11/5/2025
First Seen
Sources
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VerifiedThe per-inference cost of the MoE architecture can be reduced by 5 to 10 times compared to dense models of equivalent parameter scale.73% similarUnverifiedLocal model inference not only eliminates the risk of data leakage but also removes API call costs, resulting in lower long-term usage costs.73% similarUnverifiedThe marginal cost of AI models decreases as scale expands; the more users there are, the lower the average cost per inference after optimization through batching and GPU utilization.71% similarUnverifiedBy caching intermediate results, selectively using high-cost models, and batch processing requests, API call costs can be reduced by 30%–70%70% similarUnverifiedThe total cost of a higher-priced large model on a complete task may actually be lower.70% similar
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