Hybrid Attention Achieves 51x Inference Speedup: Large Language Models Meet Consumer Hardware

This hybrid attention mechanism—combining local windowed attention with recurrent state compression—delivers 51x inference speedups on consumer GPUs with minimal quality loss. The approach addresses a critical bottleneck in transformer models by effectively decoupling attention complexity from sequence length through KV cache paging and token compression. This represents a pragmatic alternative to pure scaling, making large language models practically deployable on resource-constrained hardware.