How to Autostart gemma-4-E4B-it on AMD/Nvidia GPU No-Internet Version

To get this model running locally in no time, utilize the built-in WSL tools.

Review and follow the instructions below.

All large files and heavy weights are downloaded automatically by the script.

The automated script takes care of everything, tailoring the setup to your specs.

🗂 Hash: ebc6a5fb77f44cc0959631d3703790bf • Last Updated: 2026-06-28 Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: minimum 16 GB for stable 8B model loading Disk Space: at least 100 GB for multiple local LLM variants Graphics: 12 GB VRAM minimum required for basic quantization

Gemma-4-E4B-it is a state‑of‑the‑art language model engineered for high‑efficiency inference on edge devices. It incorporates 2 B parameters and a 4 K context window, allowing nuanced comprehension while preserving low latency. The architecture leverages advanced quantization techniques to achieve sub‑2 ms token generation on consumer hardware. Its design includes multi‑head attention and grouped‑query attention, delivering strong performance across benchmarks such as MMLU and GSM‑8K. The model also supports seamless integration with developer tools through its open‑source API.

Parameters	2 B
Context Length	4 K tokens
Quantization	INT4
Throughput	>2000 tokens/s on GPU

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