A guide to LLM quantization methods - GPTQ, SmoothQuant, AWQ, and GGUF - and how they improve efficiency, memory use, and deployment costs.

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Quantization reduces LLM memory requirements and computational costs by converting model weights from high-precision formats (like 32-bit floats) to lower-precision representations (like 4-bit integers). The guide covers fundamental data types, explains why traditional quantization methods fail for large models due to outlier values, and details LLM-specific techniques including GPTQ, SmoothQuant, AWQ, and GGUF formats. Each method offers different trade-offs between model size, inference speed, and accuracy preservation.

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