Using Docker is the absolute quickest way to install this model on your local machine.

Follow the step-by-step instructions below.

The setup auto-downloads all needed files (several GBs).

The smart installation system will instantly find the perfect configuration for your specific hardware.

📊 File Hash: 04eefc5f43f4786d01d58d3eb32e61bb — Last update: 2026-06-22 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: free: 80 GB on system drive for scratch space Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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.

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