For the fastest local setup of this model, Docker is the best choice.
Make sure to follow the instructions below.
No manual effort needed; the setup auto-ingests the large data.
The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.
📊 File Hash: cd54127ac646e100406fe9e59f431f86 — Last update: 2026-06-26 - Processor: 4.0 GHz+ boost clock recommended for CPU inference
- RAM: 32 GB highly recommended for 26B+ GGUF models
- Disk Space: 100 GB for multi-modal model vision components
- 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) |
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