gemma-4-E4B-it with Native FP4

The fastest tactical way to launch this model locally is via a Docker image.

Refer to the action plan below to initialize the model.

The process automatically pulls down gigabytes of critical model assets.

The initial setup handles the heavy lifting, fine-tuning the environment for your device.

🛡️ Checksum: f0714a12d0788f9b99fb037ef7e3f950 — ⏰ Updated on: 2026-07-07 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: required: 16 GB absolute minimum for small models Disk Space: 80 GB NVMe SSD required for fast model weights loading 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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