How to Run gemma-4-31B-it-FP8-block Windows 11

The most efficient approach for a local installation is leveraging Docker containers.

Use the instructions provided below to complete the setup.

The setup auto-downloads all needed files (several GBs).

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

🔒 Hash checksum: 3a1301a56419d16d75cbd6e22cbc6ec8 • 📆 Last updated: 2026-07-04
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  • Processor: high single-core performance needed for token latency
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Disk Space: 100 GB for multi-modal model vision components
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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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