gemma-4-31B-it-FP8-block Locally via Ollama 2 For Low VRAM (6GB/8GB)

Deploying this model locally is quickest when done via Docker.

Follow the sequence of steps detailed below.

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

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

📎 HASH: 3f3f6b2c70eb4b4152ab3e273345d36f | Updated: 2026-06-24 Verify Processor: 6-core 3.5 GHz minimum required RAM: 48 GB needed to prevent memory swapping to disk Storage: extra room for future model updates and datasets Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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. FSR 3.2 frame generation backend injector for previous GPU generations
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