Quick Run gemma-4-31B-it-FP8-block on AMD/Nvidia GPU 5-Minute Setup

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🧩 Hash sum → 58c4786de8036931f4d614f3db336e4b — Update date: 2026-07-03 Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: high-speed DDR5 memory preferred for CPU offloading Storage: extra room for future model updates and datasets Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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)

1. Installer configuring local multi-agent autogen frameworks with local LLMs
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3. Installer deploying standalone local vector database engines for complex Dify workflow pools
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5. Installer configuring local context shifting for massive textbook indexing
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