Quick Run gemma-4-31B-it-FP8-block Locally via Ollama 2 with 1M Context Local Guide

junio 29, 2026

Quick Run gemma-4-31B-it-FP8-block Locally via Ollama 2 with 1M Context Local Guide

For the fastest local setup of this model, enabling Windows Features is best.

Simply follow the directions outlined below.

An automated background process downloads all required large-scale files.

The engine benchmarks your hardware to apply the most effective operational mode.

🧩 Hash sum → 09ee244fb6bdac67943c7c3692312b69 — Update date: 2026-06-29
  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: enough space for background apps and OS overhead
  • Storage:100 GB free space for HuggingFace cache folder
  • Graphics: 12 GB VRAM minimum required for basic quantization

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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