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What is quantization and when would you use a quantized model?

Q: What is quantization and when would you use a quantized model?

**TL;DR.** Quantization stores weights at **lower precision** (e.g. 4-bit ints instead of 16-bit floats), cutting memory ~4x and boosting throughput with a small quality loss. It is what makes **local and single-GPU LLMs** practical. FP16 weights 7B model ≈ 14 GB quantize INT4 weights <text class='d-sub' x='316' y='73' text-anchor='midd

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Shrinking weights from 16-bit to 4-bit: the memory/quality trade-off behind local and edge LLMs.

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Running a quantized model locally

# Ollama pulls a 4-bit GGUF by default — 8B fits in ~5 GB RAM
ollama run llama3.1:8b

# llama.cpp: pick the quantization level explicitly
./llama-cli -m models/llama-3.1-8b-Q4_K_M.gguf -p "..."
# Q4_K_M ≈ best size/quality trade-off in practice

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