Run gemma-4-E4B-it on Your PC Step-by-Step

Docker offers the quickest path to setting up this model locally.

Follow the sequence of steps detailed below.

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

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

📤 Release Hash: c573c7b2042ba609d9c82949fd98a804 • 📅 Date: 2026-06-25

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Processor: next-gen chip for heavy context processing
RAM: 48 GB needed to prevent memory swapping to disk
Disk Space: 80 GB NVMe SSD required for fast model weights loading
GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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