The YOCO architecture is a decoder-decoder model that reduces GPU memory demands while retaining global attention capabilities. It consists of a self-decoder and cross-decoder, allowing for efficient caching and reuse of key-value pairs. YOCO achieves favorable performance compared to traditional Transformers, with significant improvements in inference memory, latency, and throughput, making it suitable for large language models and long context lengths.