TurboQuant Compression Engine Achieves 37% Model Size Reduction in Llamacpp

TurboQuant's new weight compression support in Llamacpp enables significant model optimization with 27-37% size reduction while maintaining minimal performance loss (1.0-1.9% perplexity increase). This post-training quantization approach requires no retraining or calibration data, making it highly practical for production deployment. Currently Metal-optimized with CUDA and HIP support in development, this technology is crucial for reducing inference latency and deployment costs on resource-constrained environments.