gemma-4-31B-it-AWQ-4bit Using Pinokio For Low VRAM (6GB/8GB) Local Guide

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

Simply follow the directions outlined below.

The system automatically triggers a cloud download for all heavy weights.

The engine benchmarks your hardware to apply the most effective operational mode.

📊 File Hash: af9189d548b8923586d7830fbfa9527c — Last update: 2026-06-29 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: enough space for background apps and OS overhead Disk Space: at least 100 GB for multiple local LLM variants Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

The Gemma-4-31B-it-AWQ-4bit model is a 31‑billion parameter instruction‑tuned language model optimized for efficient inference. It leverages AWQ quantization to achieve 4‑bit precision while preserving much of the original performance. The model supports a 2048‑token context window, enabling coherent long‑form generation. Benchmarks show it rivals larger models on reasoning, coding, and multilingual tasks despite its reduced memory footprint. Its compact design makes it suitable for deployment on consumer‑grade hardware and edge devices. The following table compares key specifications with related models:

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