Expired50% confidenceFactTime unknown
MFU (Model FLOPs Utilization) measures the ratio of actual compute consumed during model training to the GPU's theoretical peak compute capacity
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
The "Worse is Better" Philosophy of Large Model Design: Why Simple and Brutal Beats Refined and Complex
bilibiliAI大模型基地
Related Claims
UnverifiedMFU的计算公式为MFU=(实际模型FLOPs)/(硬件理论峰值FLOPs×训练时间)，其只计算模型本身有效计算，不包含重计算等额外开销，区别于HFU76% similarUnverifiedModel fine-tuning typically requires a large investment in annotated data and GPU computing resources.69% similarUnverifiedDeepMind's Chinchilla scaling law demonstrates that the optimal ratio of training data volume to model parameter count evolves over time, and equal FLOP investment can produce models with significantly different capabilities68% similarUnverifiedSnell等人2024年的研究量化了当模型规模固定时，增加推理算力的边际收益在许多任务上优于同等算力用于继续预训练67% similarUnverifiedIn LLM inference scenarios, GPU memory capacity is often more critical than peak compute performance.67% similar
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