Run gemma-4-31B-it Using Pinokio with 1M Context Step-by-Step

For an instant local deployment, running a pre-configured shell script is ideal.

Follow the straightforward walkthrough provided below.

All large files and heavy weights are downloaded automatically by the script.

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

🧾 Hash-sum — 4b6ecad3188479f40abf2ed3a7de7e1d • 🗓 Updated on: 2026-06-29



  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The Gemma-4-31B-it model represents a significant advancement in open‑source language models, combining a 31 billion parameter architecture with sophisticated instruction tuning. It leverages a mixture‑of‑experts design to achieve both high performance and computational efficiency, making it suitable for a wide range of commercial and research applications. The model supports multimodal inputs, allowing users to process text, images, and audio within a unified framework. Benchmark evaluations place it among the top‑tier models in reasoning, coding, and factual knowledge tasks, often matching or surpassing proprietary alternatives. An accompanying

provides detailed technical specifications and a comparative performance snapshot against earlier Gemma releases.

Specification Value
Parameters 31 B
Context Length 8 K tokens
Training Data Web‑scale multilingual corpus
Inference Speed ~120 MFLOPS
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