Model7d 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. Practitioners gain faster inference speeds and lower VRAM usage. This trend prioritizes sustainable scaling over raw parameter growth to make long-form processing viable for developers.