Optimizing Quantized LLMs on On-Premise Hardware: An Experimental Approach
A user explores strategies to stabilize heavily quantized Large Language Models on local hardware setups with 80GB VRAM. The goal is to mitigate unpredictable outputs, often associated with quantized models, by calibrating sampling parameters like `temperature` and `top_p`, offering valuable insights for efficient on-premise deployments and output quality control.