How to Autostart gemma-4-31B-it-FP8-block Windows 10 Local Guide

If you want the fastest local installation for this model, use Docker.

Review and follow the instructions below.

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

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

đź–ą HASH-SUM: 3193b1daebc13ea35e51cad037d49ee1 | đź“… Updated on: 2026-06-26
  • Processor: next-gen chip for heavy context processing
  • RAM: required: 16 GB absolute minimum for small models
  • Storage:100 GB free space for HuggingFace cache folder
  • 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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