The conventional approach to AI processing relies on GPUs to store intermediate outputs and recompute them before transferring them to the next GPU. This approach results in a significant memory limitation, regardless of the GPU’s processing speed. Liu explained, “The goal of this research was to remove this bottleneck.”
The “Ring Attention” method transforms the traditional setup by creating a ring of interconnected GPUs, allowing for simultaneous data transfer between devices. Liu elaborated, “Each GPU holds one query block, and key-value blocks traverse through a ring of GPUs for self-attention and feedforward computations in a block-by-block fashion.” This ingenious approach preserves the original Transformer architecture but reorganizes computation, enabling the context size to scale with the number of GPUs.
The implications of this innovation are staggering. Instead of being confined to processing tens of thousands of words, AI models can now potentially handle millions of words, entire codebases, or extensive video content within a single context window.
