Decoupled Attention from Weights for Gemma 4.26B
Clone the larql repo and experiment with decoupled attention on Gemma 4.26B to bypass local LLM scaling limits.
Clone the larql repo, run the split‑attention demo on Gemma 4.26B, and benchmark scaling.
Summary
Gemma 4.26B now supports decoupled attention, separating the attention computation from the weight storage. The technique splits the workload between a local machine handling attention and a secondary machine—often a cheap Xeon—storing the weights, effectively bypassing the scale limitations of local LLMs. The functional implementation is available in the larql GitHub repository (https://github.com/chrishayuk/larql). A video demonstration (https://www.youtube.com/watch?v=1jGR4zqpyKA) provides an overview of the approach.
This method allows developers to run larger models on modest hardware by offloading the heavy weight matrices, reducing memory pressure on the primary inference machine. The split‑attention design can be integrated into existing local LLM workflows without significant architectural changes.
Key changes
- Decoupled attention from weights in Gemma 4.26B
- Splits attention computation onto a separate local machine
- Bypasses scaling limitations of local LLMs
- Provides functional code in GitHub repo https://github.com/chrishayuk/larql
- Includes video overview link
- Enables use of cheap Xeon for weights while local machine handles attention