Jul 3 – 9, 2023

Do large language models really need all those layers?

Amazon's research reveals that large language models contain significant redundancy—70% of attention heads and 20% of feed-forward networks can be removed without degrading performance. This challenges assumptions about LLM architecture efficiency and suggests models are undertrained, with major implications for inference cost and model compression.