gemma-4-31B-it-FP8-block on AMD/Nvidia GPU No Admin Rights Step-by-Step

The fastest method for installing this model locally is by using Docker.

Follow the sequence of steps detailed below.

1-click setup: the app automatically fetches the large weight files.

The smart installation system will instantly find the perfect configuration for your specific hardware.

📘 Build Hash: acff84b5a7c4e438b0a3851e934f508c • 🗓 2026-06-25



  • Processor: high single-core performance needed for token latency
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk Space: 100 GB for multi-modal model vision components
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The **gemma-4-31B-it-FP8-block** model represents a significant advancement in open‑source language models, combining a **31 billion parameters** base with an *in‑struct tuned* configuration optimized for interactive tasks. Built on the latest *Gemma* architecture, it leverages *FP8 block* quantization to deliver high performance while maintaining a relatively small memory footprint. The model supports a **128K token context window**, enabling it to handle long‑form conversations and complex reasoning without truncation. In benchmarks, it outperforms comparable 31B models by over **12%** on reasoning tasks while consuming less than **16 GB** of GPU memory during inference. A concise

summarizing its core specs is provided below for quick reference.

Parameter Count 31 B
Context Length 128K tokens
Precision FP8 block
Architecture Gemma (in‑struct tuned)
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دسته‌بندی‌ها: EXL2