The Complexity of Quantization for On-Premise LLMs

The increasing adoption of Large Language Models (LLMs) has driven research towards solutions that enable more efficient deployment, particularly in self-hosted or air-gapped environments. Quantization emerges as a key technique to reduce model sizes and VRAM requirements, making them accessible on less powerful hardware. However, the choice of quantization method and its implementation can lead to significant differences in the final model characteristics, as demonstrated by a comparative analysis of Gemma-4 models.

Recently, an investigation compared quantized versions of Gemma-4, specifically Google's Q4_0 models and Unsloth's Q4_K_XL. The initial observation revealed an unexpected discrepancy: Google's Q4_0 models, such as the E4B version, exhibited a larger file size (5.15 GB) compared to Unsloth's Q4_K_XL counterparts (4.22 GB) for the same base model. This anomaly raises questions about the nature of different quantization strategies and their practical implications.