gemma-4-31B-it-AWQ-4bit Using Pinokio One-Click Setup Direct EXE Setup

gemma-4-31B-it-AWQ-4bit Using Pinokio One-Click Setup Direct EXE Setup

A standalone PowerShell module provides the fastest route to local installation.

Check out the detailed setup guide below to begin.

The installer automatically pulls the model (could be multiple GBs).

To save you time, the system will automatically determine efficient resource allocation.

📄 Hash Value: bb1b5a726f8367a526375c0493272b65 | 📆 Update: 2026-07-08
  • Processor: 6-core 3.5 GHz minimum required
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk: high-speed SSD 120 GB to cache model layers
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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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