Model Quantization

Model Quantization converts neural network weights and activation tensors from high-precision floating-point formats (FP16/BF16) to low-bitwidth integers (such as INT8, INT4, AWQ, GPTQ, or GGUF). This slashes GPU memory footprints and boosts serving throughput while requiring rigorous empirical benchmarking on downstream tool accuracy.