KV sharing and compressed attention now drive efficiency in models like Gemma 4 and DeepSeek V4. These techniques reduce the memory overhead required to process massive prompts. This shift lowers inference costs for developers. It enables longer context windows without requiring proportional increases in expensive H100 GPU memory.