Deploy gemma-4-E4B-it-MLX-4bit Zero Config

Docker offers the quickest path to setting up this model locally.

Simply follow the directions outlined below.

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The setup auto-downloads all needed files (several GBs).

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

🔗 SHA sum: e56945a124f65ab3ed552d24e50f74ca | Updated: 2026-06-28 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 32 GB or higher for smooth 32k context lengths Storage:100 GB free space for HuggingFace cache folder Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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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