How to Autostart gemma-4-E4B-it-MLX-4bit Locally via LM Studio For Low VRAM (6GB/8GB) 5-Minute Setup

Running this model locally is fastest when deployed through Docker.

Make sure to follow the instructions below.

The setup auto-streams the model assets (expect a multi-GB download).

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

🔒 Hash checksum: 053fc8b6abec5dfde1b7bcc728045444 • 📆 Last updated: 2026-06-22



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Storage: extra room for future model updates and datasets
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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