AI2026-05-17

Quantization

Also known as: Quantization / 量子化 / モデル量子化

Converting model weights from 32-bit or 16-bit floats to lower-precision formats (8-bit, 4-bit, etc.) to reduce model size and memory footprint, enabling faster inference and local execution.

Overview

Quantization represents model weights in lower bit-width formats (8-bit, 4-bit) to shrink file size and VRAM footprint. Common formats include GGUF (llama.cpp), GPTQ, AWQ, and NF4 (QLoRA). 4-bit quantization reduces model size roughly 4x vs float16 with minimal accuracy loss.

Enabling local LLM execution

Tools like Ollama distribute pre-quantized models, allowing 7B-13B class models to run on 8-16 GB VRAM consumer GPUs or Apple Silicon. This keeps business data local, addressing privacy concerns while eliminating cloud API costs.

Gemma 4 needs 5GB VRAM (E2B/E4B), 16GB (26B MoE), or 24-62GB (31B Dense) depending on quantization. Requirements by model: RTX 3060 to H100, Apple Silicon M1-M4, CPU-only operation, RAM sizing, and budget builds. Updated July 2026.

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GGUF Quantization: Which Level to Pick (Q4_K_M, Q5_K_M, Q8_0, IQ) for Local LLMs

Start with Q4_K_M; step up to Q5_K_M or Q6_K if you have VRAM headroom. This guide explains GGUF naming, the quality/speed/VRAM tradeoffs per level, IQ (imatrix) quants, and how to choose by task. Updated July 2026.

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