Launch gemma-4-12B-it Using Pinokio

The fastest way to get this model running locally is via Docker.

Simply follow the directions outlined below.

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The setup auto-streams the model assets (expect a multi-GB download).

The smart installation system will instantly find the perfect configuration for your specific hardware.

💾 File hash: bde37ea612d7f3287a7893aa136480ef (Update date: 2026-06-23) Verify CPU: 8-core / 16-thread recommended for orchestration RAM: required: 16 GB absolute minimum for small models Storage: extra room for future model updates and datasets Graphics: 12 GB VRAM minimum required for basic quantization

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:

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