Install gemma-4-12B-it-QAT-GGUF on Copilot+ PC with 1M Context 5-Minute Setup

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

Make sure you implement the steps mentioned below.

An automated background process downloads all required large-scale files.

Without any user input, the software calibrates parameters for optimal hardware usage.

📘 Build Hash: e832548f8e5391b37913851af16f1a1f • 🗓 2026-06-28 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: free: 80 GB on system drive for scratch space Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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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