The original title is: "LLM Quantization Levels Compared: Q4_K_M vs Q8_0 vs FP16 [2026]"

Short summary

Q4_K_M is the optimal default quantization for consumer LLM inference — it cuts memory by 65% versus FP16 while preserving 95-97% quality and doubling throughput. Q8_0 is effectively lossless for structured tasks. Model size matters more than quantization precision: a 70B at Q4_K_M typically outperforms a 7B at Q8_0.

• •Q4_K_M is Pareto-optimal: 65% less VRAM than FP16, 2-3× faster, only 3-5% quality loss
• •Q8_0 is lossless for coding/reasoning tasks; 2× VRAM cost justified for structured work
• •Model size beats quantization precision: 70B at Q4_K_M outperforms 8B at Q8_0

Generated with AI, which can make mistakes.