Q4_K_M vs Q8_0 vs FP16: How to Choose the Right LLM Quantization Level

LLM quantization reduces model weight precision from 32-bit or 16-bit down to 8-bit or 4-bit, enabling large AI models to run on consumer-grade hardware instead of expensive datacenter GPUs. A 7-billion-parameter model that requires roughly 14 GB of memory at FP16 precision can fit into approximately 4 GB when quantized to Q4_K_M. Q4_K_M is widely considered the optimal default for most consumer setups, delivering an estimated 95–97% of FP16 quality at around 35% of the VRAM cost and two to three times the inference speed. Q8_0 is nearly lossless compared to FP16 and is recommended for tasks requiring precision, such as coding or structured reasoning, at roughly double the VRAM of Q4_K_M. As of 2026, newer IQ-quants using importance-matrix quantization are reported to outperform traditional K-quants at equivalent bit widths, representing the next step forward for local inference.