Model1d ago
New LLMs Slash Long-Context 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 and lower VRAM requirements. This trend makes processing million-token documents viable for smaller, open-weight deployments.