How to Run a Neural Network More Cheaply: Google's TurboQuant Optimizes Memory
Google has unveiled TurboQuant, a KV cache compression method that can cut inference memory by at least a factor of six and speed up attention computation. That matters most for running LLMs locally and for long conversations. The technique targets not the model's weights but its working memory during generation. But it is unlikely to stop the growth in overall AI infrastructure spending.
AI-processed from ZDNet AI; edited by Hamidun News
On March 24, Google Research developed TurboQuant, an algorithm for local neural networks that reduces memory requirements while the model generates a response.
What is the KV cache in neural networks?
It is the working memory in which a language model stores intermediate keys and values for tokens it has already processed. The longer the conversation or document, the more memory this cache takes up, and the more expensive it becomes to run the model.
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