Full Deployment gemma-4-E4B-it-MLX-4bit For Low VRAM (6GB/8GB)

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

Use the instructions provided below to complete the setup.

The installer automatically pulls the model (could be multiple GBs).

The deployment tool scans your environment and automatically chooses the ideal parameters for your OS.

📘 Build Hash: 707209d152d2578f8f168cfb1ffacb57 • 🗓 2026-06-22 Verify Processor: high single-core performance needed for token latency RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: required: fast PCIe 4.0 drive for instant boots Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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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