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How to Autostart gemma-4-E4B-it-MLX-4bit on Copilot+ PC Full Method

29 juin 2026.Annouck Forcadell

How to Autostart gemma-4-E4B-it-MLX-4bit on Copilot+ PC Full Method

If you want the fastest local installation for this model, use Docker.

Please follow the instructions listed below to get started.

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

The smart installation system will instantly find the perfect configuration for your specific hardware.

🔧 Digest: c64ae2e00d3c05d3818d83b84102ee67 • 🕒 Updated: 2026-06-26



  • Processor: high single-core performance needed for token latency
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

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.

Parameters 4.5 B
Quantization 4‑bit
Context Length 8K tokens
Inference Speed <10 ms
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