GGUF Quantization Explained: Q4_K_M vs Q5_K_M vs Q8 — Which to Pick If you're running local LLMs with llama.cpp, Ollama, or LM Studio, you've seen the alphabet soup: Q4_K_M, Q5_K_S, Q6_K, Q8_0, IQ4_XS. Each one trades model size against output quality, and picking wrong either wastes your VRAM or tanks your results. This guide cuts through the noise. We benchmarked every common quantization level and measured the actual accuracy tradeoffs so you can pick the right one for your hardware. What Is GGUF Quantization? A full-precision LLM stores every weight as a 16-bit floating point number (FP16). A 7B parameter model at FP16 weighs ~14 GB. Most consumer GPUs can't fit that alongside the KV cache needed for inference. Quantization compresses those weights to lower precision — 8-bit, 5-bit, even 4-bit — dramatically shrinking the model. A Q4_K_M version of that same 7B model fits in ~4.4 GB, making it runnable on an 8 GB GPU with room for context. The "GGUF" part is just the file format.…