Multi-Token Attention is a proposed module that enhances the attention mechanism by implementing convolutional operations over queries, keys, and heads. This approach allows nearby tokens to influence each other's attention weights, aiming for more precise attention in representation learning. The conversation explores comparisons with Byte Latent Transformer (BLT), which focuses on attention mechanisms at the embedding level. Users express concerns about scalability and memory bandwidth bottlenecks, especially with the increasing context sizes in large language models (LLMs). There is interest in the resurgence of convolutional techniques in LLM architectures, highlighting recent developments such as the hyena operator that demonstrate successful implementations. These discussions point toward the balancing act between improving model performance through added computational resources and ensuring efficiency at scale.