Deploy gemma-4-E4B-it-MLX-4bit on AMD/Nvidia GPU For Low VRAM (6GB/8GB)

Deploying this model locally is quickest when done via Docker.

Make sure to follow the instructions below.

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

The installer will automatically analyze your hardware and select the optimal configuration for your system.

🛠 Hash code: 36173cc6c2877cb1ae3e1ab18f1a2efa — Last modification: 2026-06-25 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space:70 GB free space for full FP16 weights storage 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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