LLM Quantization Performance Testing - Ollama and LM Studio Benchmark Guide

Explore the performance implications of LLM quantization through a comprehensive 19-minute benchmark analysis comparing different quantized models for local deployment on PCs and laptops. Learn which quantization methods to choose when running large language models locally through Ollama or LM Studio, particularly when dealing with limited VRAM constraints on NVIDIA GPUs. Discover the trade-offs between model size reduction and performance degradation across various quantization levels, with specific focus on DeepSeek R1 models ranging from the compact 404GB q4_K_M version to the full 1.3TB FP16 implementation. Understand the practical considerations for selecting optimal quantization settings based on your hardware limitations while maintaining acceptable AI model performance. The analysis draws from systematic research on LLM quantization performance, energy consumption, and quality metrics to provide evidence-based recommendations for local AI infrastructure deployment.