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Quantization Formats — GGUF Deep Dive

Q4_K_M vs Q8_0 vs FP16 — find the production sweet spot

+100 XP5 min2 / 10

Overview: Quantization Formats — GGUF Deep Dive

Overview: Quantization Formats — GGUF Deep Dive

GGUF Q4_K_M loses only ~0.15 perplexity points vs FP16 while using 4× less memory. Q2_K loses ~1.2 perplexity — usually unacceptable. Q8_0 is near-lossless at 0.02 perplexity loss. The 'K' suffix means K-means clustering improves weight distribution within each quantization block.

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