Run Gemma 4 12B on 8GB RAM with Unsloth Dynamic GGUFs

Melvin Vivas · X post · 2026-06-04 · Open on X

Topics: LLM Fundamentals, Fine-tuning & Model Customization, AI Dev Tools & Productivity · Level: intermediate

Summary

Unsloth's Dynamic GGUF quantizations let Gemma 4 12B run locally on only 8GB of RAM. The model supports image and audio input and a 256K context window, and Unsloth Studio can both run and fine-tune it.

Key points

• Gemma 4 12B can run locally on just 8GB RAM using Unsloth Dynamic GGUF quantizations.
• Gemma 4 12B Unified is multimodal and supports image and audio input.
• It has a 256K-token context window.
• Unsloth Studio can run and train (fine-tune) the model.
• GGUF weights are on Hugging Face under the unsloth org, and Unsloth's docs have a Gemma 4 guide.

Resources mentioned

• Unsloth Gemma 4 12B IT GGUF (Hugging Face) · tool · huggingface.co · free · open in a browser to verify
  Quantized Dynamic GGUF weights of Gemma 4 12B instruct for low-RAM local inference.
• Unsloth Documentation - Gemma 4 guide · docs · unsloth.ai · free
  Unsloth's guide to running and fine-tuning Gemma 4 models.
• Unsloth · tool · unsloth.ai · free
  Open-source library for fast, memory-efficient fine-tuning of open LLMs (LoRA/QLoRA) on a single GPU or in Colab.
  Also in: Run Laya Decision models locally with Unsloth on 4GB RAM (Melvin Vivas on X · notes), Unsloth passes 500M model downloads on Hugging Face (Melvin Vivas on X · notes), Run Qwen-Image-2.1 locally on 12GB VRAM with Unsloth GGUFs (Melvin Vivas on X · notes), Base vs fine-tuned Gemma 4 E2B as a model router (Melvin Vivas on X · notes) and 29 more
• Gemma 4 12B · tool · blog.google · free · open in a browser to verify
  Google's open-weight, encoder-free multimodal model under Apache 2.0 for on-device use.
  Also in: Run Gemma 4 12B locally with LM Studio (Melvin Vivas on X · notes), Gemma 4 12B: encoder-free multimodal model for laptops (Melvin Vivas on X · notes)

Try this

• Download the Unsloth Gemma 4 12B GGUF and run it locally on an 8GB RAM machine.
• Follow the Unsloth Gemma 4 guide to fine-tune the model.
• Fine-tune Gemma 4 12B on your own dataset with Unsloth Studio and run it locally.