How to Run gemma-4-12B-it-qat-w4a16-ct Using Pinokio with Native FP4

If you want the fastest local installation for this model, use Docker.

Follow the step-by-step instructions below.

1-click setup: the app automatically fetches the large weight files.

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

📤 Release Hash: 79b85afb293935770bd1cf282f299e93 • 📅 Date: 2026-06-22 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: at least 32 GB in dual-channel mode for bandwidth Storage: extra room for future model updates and datasets Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

The **gemma-4-12B-it-qat-w4a16-ct** model represents a significant advancement in instruction‑tuned language models, combining a 12‑billion parameter base with a specialized QAT quantization scheme. It leverages a *w4a16* format, meaning weights are stored in 4‑bit precision while activations remain in 16‑bit floating point, delivering a balanced trade‑off between memory footprint and computational accuracy. The model has been optimized through **QAT**, which fine‑tunes the network to mitigate quantization errors and preserve performance across diverse tasks. In benchmark evaluations, it consistently outperforms comparable 12B‑parameter models while requiring roughly 60 % less GPU memory, making it ideal for deployment on resource‑constrained edge devices. A quick reference table below compares its key attributes with other popular Gemma variants, highlighting its superior efficiency and accuracy metrics.

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