Context is Everything: Why Maximum Sequence Length Matters - Cerebras

This post explains the quadratic complexity of attention layers in transformer models, where memory and compute grow by a factor of 4 for every 2x increase in sequence length. It contrasts this with previous attempts to reduce complexity (sparsification, hierarchy, approximation) which introduce presumptions about data patterns. The post then details how Cerebras' Wafer-Scale Engine (WSE), with its 850,000 PEs, ultra-low latency fabric, and weight streaming feature, enables processing of sequences up to 50,000 tokens with dense attention, a 20x increase over modern GPUs. This is achieved by keeping activations on the wafer and loading weights layer by layer, freeing up memory and leveraging on-chip network bandwidth. The post also presents results showing improved next token prediction accuracy with longer sequences and highlights potential applications in document summarization, drug discovery, and genomics.