New Transformer Architecture Could Enable Powerful Llms Without Gpus

Researchers have developed a novel architecture for language models that eliminates matrix multiplications, reducing memory usage and latency during training and inference. This breakthrough allows for larger language models to be trained and run more efficiently, making them essential for tasks like transforming input data through layers of a neural network to make predictions.