Install gemma-4-31B-it-AWQ-4bit PC with NPU Direct EXE Setup

Install gemma-4-31B-it-AWQ-4bit PC with NPU Direct EXE Setup

For the fastest local setup of this model, Docker is the best choice.

Follow the sequence of steps detailed below.

During setup, the script automatically determines and applies the best settings tailored to your machine.

đź’ľ File hash: 4f2b307b45db73f2a122083f8e7932d0 (Update date: 2026-06-24)



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Disk Space: 100 GB for multi-modal model vision components
  • Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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:

Model Parameters Quantization Context Length Avg. Benchmark
Gemma-4-31B-it-AWQ-4bit 31B 4-bit AWQ 2048 84.3
Llama-2-70B 70B 16-bit 4096 86.1
Mistral-7B-v0.1 7B 16-bit 8192 78.5
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