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

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

Make sure to follow the instructions below.

1-click setup: the app automatically fetches the large weight files.

The installer will automatically analyze your hardware and select the optimal configuration for your system.

🛡️ Checksum: 05df5de4e86d661b857a4bdc664837d7 — ⏰ Updated on: 2026-06-24 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB highly recommended for 26B+ GGUF models 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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