Deploying this model locally is quickest when done via Docker.

Follow the guidelines below to continue.

The setup auto-downloads all needed files (several GBs).

The automated installation script takes care of everything by tailoring the setup perfectly to your system specs.

🔐 Hash sum: fa878a1c9bc5b2c000963f59607cc922 | 📅 Last update: 2026-06-27 Verify Processor: 6-core 3.5 GHz minimum required RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: at least 100 GB for multiple local LLM variants Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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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