Run gemma-4-31B-it-AWQ-4bit on Your PC For Low VRAM (6GB/8GB) Direct EXE Setup

Using Docker is the absolute quickest way to install this model on your local machine.

Simply follow the directions outlined below.

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No manual effort needed; the setup auto-ingests the large data.

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

📤 Release Hash: 0d92ee5a7eafd59d793e67da975d9d3b • 📅 Date: 2026-06-26 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 32 GB or higher for smooth 32k context lengths Disk Space: free: 80 GB on system drive for scratch space Graphics: 12 GB VRAM minimum required for basic quantization

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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