Researchers from MIT and other institutions have developed a new technique to run large language models (LLMs) more efficiently by eliminating matrix multiplication from the process. This innovation, detailed in the 'Scalable MatMul-free Language Modeling' paper, utilizes an optimized kernel during inference, significantly reducing memory consumption by more than 10 times. The new approach enables LLMs to solve natural language, math, data analysis, and symbolic reasoning tasks by generating programs. This breakthrough, published on @arxiv, could potentially transform AI technology and its applications.
Researchers upend AI status quo by eliminating matrix multiplication in LLMs https://t.co/WaPw97tNse
for all the hype, this recent work shows that LLMs are not good at abstract reasoning https://t.co/ctzKgCM1ya https://t.co/o8o9EDLYxK
Research scientists face great demands on their time. Large language models could help by speeding up the writing of papers, thereby freeing up time for scientists to develop new ideas, collaborate or check for mistakes in their work https://t.co/kKe6cbXG0t 👇
Eliminating matrix multiplication in LLMs, while maintaining strong performance at billion-parameter scales with 'Scalable MatMul-free Language Modeling' paper. 🔥 This is by utilizing an optimized kernel during inference. Memory consumption can be reduced by more than 10×… https://t.co/DQ6yGJDoHr
Researchers Upend AI Status Quo by Eliminating Matrix Multiplication in LLMs #AI #AItechnology #artificialintelligence #llm #machinelearning #MatMul https://t.co/EviGSqJUio https://t.co/ZUnVGYt0CC
Software engineers develop a way to run AI language models without matrix multiplication @arxiv https://t.co/Xz7BT4aEzd
Researchers from @MIT and elsewhere have proposed a new technique that enables large language models to solve natural language, math and data analysis, and symbolic reasoning tasks by generating programs. https://t.co/nQ5IRJp9nl
Interesting read: Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. https://t.co/mqbVfW7YUI