Deploying this model locally is quickest when done via a simple curl command.

Follow the step-by-step instructions below.

The script takes care of fetching the multi-gigabyte model weights.

The initial setup handles the heavy lifting, fine-tuning the environment for your device.

📤 Release Hash: a2db27a9f12c21e914d0cf9f1a4ff3e1 • 📅 Date: 2026-07-05 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB or higher for smooth 32k context lengths Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: CUDA Compute Capability 8.0+ required for flash-attention

The **gemma-4-12B-it-QAT-GGUF** model is a 12‑billion parameter instruction‑tuned language model designed for high performance and efficiency. It leverages *QAT* (quantized aware training) and the GGUF format to achieve a *balanced trade‑off* between accuracy and inference speed on consumer hardware. The model supports a context window of up to **8192** tokens, enabling it to understand and generate longer passages with coherent reasoning. Benchmarks show it outperforms comparable open models in reasoning and coding tasks while maintaining a modest memory footprint. Below is a quick comparison of its core specifications to illustrate how it stands against other popular open models:

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