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New Transformer architecture could enable powerful LLMs without GPUs
MatMul-free LM removes matrix multiplications from language model architectures to make them faster and much more memory-efficient.
This parallelism allows GPUs to handle the large-scale computations required in deep learning much faster than traditional CPUs, making them essential for training and running complex neural network models efficiently. In traditional Transformer models, this is typically achieved using self-attention mechanisms, which use MatMul operations to compute relationships between all pairs of tokens to capture dependencies and contextual information. “These results highlight that MatMul-free architectures are capable achieving strong zero-shot performance on a diverse set of language tasks, ranging from question answering and commonsense reasoning to physical understanding,” the researchers write.
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