gemma-4-E4B-it-MLX-4bit

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

Follow the guidelines below to continue.

Then, simply start the container with the provided Docker command.

📘 Build Hash: d7e50de3c0931e405eefc5818058d3a0 • 🗓 2026-06-27 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 32 GB or higher for smooth 32k context lengths Storage: extra room for future model updates and datasets Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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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