New LLM Quantization Method Outperforms Existing Approaches

This is exciting news for anyone working with local LLMs! A user has found that MXFP4 quantization, often overlooked due to its smaller size, actually delivers better performance than Q4_K_M and Q4_K_XL in terms of perplexity. This discovery could revolutionize how we optimize LLMs for speed and efficiency.

• •MXFP4 quantization, despite being smaller, outperforms Q4_K_M and Q4_K_XL.
• •The research was conducted using llama.cpp and tested on GLM-4.7-Flash and Nemotron-3-nano models.
• •The findings were based on perplexity scores, a measure of how well a model predicts text.