  This micro-paper describes a trick to speed up inference of transformers with
RoPE (such as LLaMA, Mistral, PaLM, and Gemma). For these models, a large
portion of the first transformer layer can be precomputed, which results in
slightly lower latency and lower cost-per-token. Because this trick optimizes
only one layer, the relative savings depend on the total number of layers. For
example, the maximum savings for a model with only 4 layers (such as Whisper
tiny) is limited to 25%, while a 32-layer model is limited to 3% savings. See
https://github.com/OpenMachine-ai/transformer-tricks for code and more
transformer tricks.
