The shortest path to running this model is by activating Hyper-V features.

Go through the configuration rules shown below.

All large files and heavy weights are downloaded automatically by the script.

The script runs a quick hardware check to dynamically adjust parameters for elite speed.

💾 File hash: 239638be2b82310a6b38b12f0f44423d (Update date: 2026-07-05) Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

The Gemma-4-12B-it model delivers state‑of‑the‑art performance across a wide range of language tasks. Its 12‑billion parameter architecture enables fast inference while maintaining high accuracy on reasoning benchmarks. The model supports a 2048‑token context window, allowing it to understand longer passages and generate coherent responses. Trained on diverse web‑scale datasets, it exhibits strong multilingual capabilities and a nuanced understanding of technical terminology. Compared to its predecessors, Gemma‑4‑12B‑it shows a 15% improvement in reading comprehension and a 10% boost in code generation tasks. The following table summarizes its key specifications:

Parameter Count	12 billion
Context Length	2048 tokens
Training Data	Web‑scale multilingual corpus
Reading Comprehension	85% accuracy
Code Generation	78% pass@1

1. Script automating local installation of Open-WebUI with Docker Desktop
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