Run gemma-4-E4B-it-MLX-4bit with Native FP4

Using a native PowerShell script is the absolute quickest way to install this model.

Just follow the guidelines provided below.

The script takes care of fetching the multi-gigabyte model weights.

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

🗂 Hash: f32f84a5d524763d3f205e789b25e7e6 • Last Updated: 2026-06-26
  • Processor: next-gen chip for heavy context processing
  • RAM: minimum 16 GB for stable 8B model loading
  • Storage:100 GB free space for HuggingFace cache folder
  • 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.

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