Model1d ago
New LLMs Slash Long-Context Memory Costs
KV sharing and compressed attention now drive efficiency in models like Gemma 4 and DeepSeek V4. These architectural shifts reduce the memory overhead required for massive context windows. Developers gain faster inference speeds without sacrificing retrieval accuracy. This trend prioritizes sustainable scaling over raw parameter growth to lower operational costs for practitioners.