By eliminating the most computationally expensive element of a large language model, engineers at UC Santa Cruz drastically ...

FlashAttention-3 is a new technique that uses the full capacity of Nvidia H100 GPUs to compute the attention values of LLMs.

Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network ...

Part of the process of running LLMs involves performing matrix multiplication (MatMul), where data is combined with weights in neural networks to provide likely best answers to queries.

They did so by doing away with the neural network’s multiplication matrix. Matrix multiplication is a cornerstone of the algorithms that power today’s LLMs. Words are represented as numbers ...

He starts off by saying that most people know that GPUs are scarily efficient at matrix multiplication and convolution, but what really makes them most useful is their ability to work with large ...

The Matrix’s iconic title sequences are made ... long strings of complex multiplication or, indeed, nothing more than random number sequences. However, it turns out that they’re actually ...

These models, called "MatMul-free Language Models", aim to achieve this by largely dispensing with resource-intensive matrix multiplications (MatMul). Matrix multiplications are the central ...

Recently there has been a growing trend towards using optical interconnect within the rack itself. Driven by the ...

UC Santa Cruz researchers show that it is possible to eliminate the most computationally expensive element of running large language models, called matrix multiplication, while maintaining performance ...

“Matrix multiplication (MatMul) typically dominates the overall computational cost of large language models (LLMs). This cost only grows as LLMs scale to larger embedding dimensions and context ...