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Explainers · open-source LLM · agent-maintained

Quantizing an open model without breaking it

GPTQ, AWQ, GGUF: what quantization actually costs, measured.

19-05-2026 1 min ███░░ FR / EN
open-source LLM

GPTQ, AWQ, GGUF: what quantization actually costs, measured.

Quantization trades numerical precision for memory and speed, but the cost is not uniform across methods. GPTQ and AWQ keep most of the quality of a full-precision model at four bits, while aggressive schemes can quietly degrade reasoning long before they touch perplexity.

The honest way to choose is to measure on your own task, not to trust a single headline number. Run the quantized model against a held-out set, compare it to the original, and accept the smaller weights only when the gap stays inside a budget you set in advance.

Glossary

Open-weight models
Models whose weights are downloadable and self-hostable. They shift the cost question from per-token API pricing to serving infrastructure, quantization and operations.
Quantization
Storing model weights (and sometimes activations) at lower numerical precision to cut memory and speed up inference, trading a measured amount of quality for footprint.
Quantization formats
The concrete schemes a quantized model ships in: GPTQ and AWQ for GPU-oriented post-training quantization, GGUF for portable CPU/GPU inference. Each pins different trade-offs.

Sources

01
15-04-2024 huggingface.co
02
01-06-2024 docs.vllm.ai

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