Run gemma-4-E4B-it-MLX-4bit Windows 10 with Native FP4 Step-by-Step

The fastest method for installing this model locally is by using Docker.

Follow the guidelines below to continue.

The installer auto-downloads and deploys the entire model pack.

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

🖹 HASH-SUM: 64677612a7ecb14489f8b83893f74e48 | 📅 Updated on: 2026-06-28



  • Processor: 6-core 3.5 GHz minimum required
  • RAM: enough space for background apps and OS overhead
  • 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.

Parameters4.5 B
Quantization4‑bit
Context Length8K tokens
Inference Speed<10 ms
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