KV sharing and compressed attention now drive efficiency in Gemma 4 and DeepSeek V4. These architectural shifts reduce the memory overhead required for massive context windows. Developers can now deploy longer-context models on tighter hardware budgets. This trend prioritizes inference speed over raw parameter count to lower operational costs.