Quick Run gemma-4-E4B-it-GGUF Windows 10 For Low VRAM (6GB/8GB) Offline Setup Windows

The fastest way to get this model running locally is via Docker.

Use the instructions provided below to complete the setup.

The installer automatically pulls the model (could be multiple GBs).

The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.

📊 File Hash: 9db78ece114a7a65ca9d7ea958158867 — Last update: 2026-06-28
  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Storage: extra room for future model updates and datasets
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

The gemma-4-E4B-it-GGUF model represents a significant advancement in open‑source language models, combining efficient inference with strong reasoning capabilities. Built on the Gemma architecture, it leverages a 4‑billion parameter configuration that balances speed and accuracy for a wide range of tasks. Its context window extends to 8K tokens, enabling the model to understand longer prompts and maintain coherence across complex dialogues. In benchmark evaluations, the model achieves state‑of‑the‑art performance on reasoning, coding, and multilingual tasks while consuming minimal GPU resources. The accompanying GGUF quantization format ensures seamless integration with popular inference frameworks, reducing memory footprint and accelerating deployment. Developers and researchers can fine‑tune the model for specialized applications, benefiting from its robust tokenization and extensive community support.

Parameters 4 B
Context length 8K tokens
Quantization GGUF (Q4_K_M)
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