Rufina Santana | Install gemma-4-31B-it-FP8-block Locally via LM Studio For Low VRAM (6GB/8GB)
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Install gemma-4-31B-it-FP8-block Locally via LM Studio For Low VRAM (6GB/8GB)

01 Jul Install gemma-4-31B-it-FP8-block Locally via LM Studio For Low VRAM (6GB/8GB)

Install gemma-4-31B-it-FP8-block Locally via LM Studio For Low VRAM (6GB/8GB)

Running this model locally is fastest when deployed through a PowerShell script.

Please follow the instructions listed below to get started.

1-click setup: the app automatically fetches the large weight files.

There is no manual tuning required; the builder deploys the best matching configuration.

🗂 Hash: 15d90110a51ec3df5287e488cd133eeaLast Updated: 2026-06-26



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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