Model Quantization — shrink models for faster, cheaper inference

Reduce model weight precision from FP16 to INT8 or INT4 (or use GGUF/AWQ/GPTQ) to cut memory usage 2-4x and speed up inference with minimal quality loss.

Deployment4.4

Model Quantization — shrink models for faster, cheaper inference preview

Prompt template

N/A — this is a deployment optimization technique. Quantization configuration:

Method: AWQ or GPTQ for INT4, bitsandbytes NF4 for QLoRA
Target precision: INT4 (4-bit) or INT8 (8-bit)
Calibration dataset: 128-512 samples representative of deployment traffic
Group size: 64-128 for AWQ/GPTQ

How to use

1Choose a quantization method: GGUF (llama.cpp), AWQ, GPTQ, or bitsandbytes NF4 based on deployment target
2Prepare a small calibration dataset (128-512 samples) representative of real inference inputs
3Run calibration: the quantizer observes activation patterns to minimize precision loss
4Apply quantization to model weights, storing them at 4-bit or 8-bit with group-wise scaling
5Evaluate the quantized model on a benchmark — expect <2% quality drop for INT4 on 7B+ models
6Deploy with an inference engine optimized for the quantization format (vLLM, llama.cpp, TGI)

Pro tips

AWQ typically preserves quality better than GPTQ at 4-bit, especially for larger models
GGUF with llama.cpp is the best choice for CPU and edge deployment; AWQ/GPTQ for GPU serving
Always calibrate on data similar to production — calibrating on generic text hurts domain-specific quality
Per-group quantization (group size 64-128) preserves more accuracy than per-layer quantization

Works best with

llama-cppauto-gptqawqbitsandbytes

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