Setup gemma-4-E4B-it-MLX-4bit on AMD/Nvidia GPU Full Speed NPU Mode

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

Simply follow the directions outlined below.

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

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

🧮 Hash-code: 6e9ab4e872f9a9084efba942381396bb • 📆 2026-06-26 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 48 GB needed to prevent memory swapping to disk Disk: high-speed SSD 120 GB to cache model layers Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

The **gemma-4-E4B-it-MLX-4bit** model represents a significant advancement in open‑source language models, combining the gemma architecture with MLX optimization for ultra‑low latency inference. Built on a 4‑bit quantized backbone, it delivers high performance while consuming only a few megabytes of memory, making it ideal for edge devices and mobile applications. With **4.5 B** parameters and a context window of 8K tokens, the model balances accuracy and efficiency, achieving state‑of‑the‑art results on benchmark suites. The integrated MLX compiler further accelerates inference by optimizing kernel execution and reducing overhead, resulting in sub‑10ms response times on consumer hardware. Below is a quick comparison of key specifications that highlight why this model stands out in the current landscape.

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