Our submission applies SmoothQuant and GPTQ to the Gemma 3 12B model, producing a W8A8 (8-bit weight, 8-bit activation) compressed model. SmoothQuant is first applied to reduce activation outliers by migrating some of the quantization difficulty from activations onto the weights, rescaling so that activations become easier to quantize at the cost of slightly harder-to-quantize weights. The weights are then quantized using GPTQ and activations using RTN quantization.