Block Clustered Quantization (BCQ) is a novel technique that enables accurate 4-bit LLM inference without expensive fine-tuning or quantization-aware training.

• Achieves W4A4 quantization (4-bit weights and activations) without accuracy degradation
• Leverages block-level clustering to optimize quantization parameters across different model regions
• Minimizes computational and memory requirements while preserving model performance
• Provides a practical path to deploying efficient LLMs on resource-constrained hardware

This research represents a significant engineering advancement for deploying large language models in edge computing, mobile applications, and other scenarios with limited computational resources.

BCQ: Block Clustered Quantization for 4-bit (W4A4) LLM Inference