Originally published at kunalganglani.com — read it there for inline code, hero image, and live links.
LLM Quantization Levels Compared: Q4_K_M vs Q8_0 vs FP16 [2026]
Local LLM quantization is the process of reducing a model's weight precision — from 32-bit or 16-bit floating point down to 8-bit, 4-bit, or even lower — so it fits in consumer-grade VRAM without requiring a datacenter GPU. Every developer pulling a model through Ollama or LM Studio today faces the same decision: which quantization level actually gives the best trade-off between quality, speed, and memory? This local LLM quantization levels comparison of Q4, Q8, and FP16 is the guide I wish existed when I started running models locally.
Key takeaways:
Q4_K_M is the Pareto-optimal default for most consumer setups — roughly 95-97% of FP16 quality at ~35% of the VRAM cost and 2-3× the throughput.
