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cs.LG2025
Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs
Song Bian, Tao Yu, Shivaram Venkataraman +1
Scaling the number of parameters and the size of training data has proven to be an effective strategy for improving large language model (LLM) performance. Yet, as these models gro…
cs.LG2025
Scaling Inference-Efficient Language Models
Song Bian, Minghao Yan, Shivaram Venkataraman
Scaling laws are powerful tools to predict the performance of large language models. However, current scaling laws fall short of accounting for inference costs. In this work, we fi…