Shrinking weights from 16-bit to 4-bit: the memory/quality trade-off behind local and edge LLMs.
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What is quantization and when would you use a quantized model?
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quantizationinferenceopen-weightsself-hosting
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Quantization stores model weights (and sometimes activations) at lower numeric precision — e.g. 4-bit integers instead of 16-bit floats — cutting memory and increasing throughput 2-4x with a small quality loss. It is what makes running open-weight models on a laptop or single GPU feasible (GGUF, AWQ, GPTQ formats). Use
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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 practice05
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