GANQ addresses deployment challenges of Large Language Models by introducing a non-uniform quantization framework optimized for modern GPUs.

• Reduces memory usage while maintaining model performance
• Overcomes hardware limitations for mixed-precision computations
• Provides better representation of weight distributions than uniform quantization
• Enables more efficient inference for resource-intensive LLMs

This research matters for Engineering by offering practical solutions to hardware-software optimization challenges in AI deployment, potentially enabling broader adoption of LLMs in resource-constrained environments.

GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language Models