Model17h ago
Open-Weight Models 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 requirements. This trend makes high-token processing viable for smaller, consumer-grade hardware without sacrificing model performance.