In long-horizon AI agent workloads, KV cache I/O scales linearly with context length, requiring the full historical KV to be read for every new token generated. This linear scaling of memory bandwidth demand becomes the dominant bottleneck during decode, especially with large context windows like Kimi K3's 1M tokens.
Memory bandwidth is the binding constraint for long-context agentic AI, with demand growing linearly as context accumulates.