Quantization: GPTQ vs AWQ
Compare post-training quantization models for squeezing massive LLMs onto standard GPUs.
Abstract Algorithms
Quick Take
Quantization reduces the memory footprint of an LLM by converting weight numerical values from 16-bit floating points (FP16) to 4-bit or 8-bit integers. This allows massive models to run on affordable
Quantization reduces the memory footprint of an LLM by converting weight numerical values from 16-bit floating points (FP16) to 4-bit or 8-bit integers.
This allows massive models to run on affordable consumer GPUs with negligible accuracy loss.
βοΈ GPTQ vs AWQ Comparison
GPTQ (Layer-wise Calibration)
- Method: Quantizes weights layer-by-layer using calibration data, optimizing for execution speed on GPUs.
- Best for: Raw token throughput and batch inference.
AWQ (Activation-aware Weight Quantization)
- Method: Protects the most important 1% of weights (salient weights) from quantization by looking at activation distributions.
- Best for: Keeping high accuracy on smaller models (like Llama 8B) at low bitrates.
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