Run gemma-4-E4B-it-MLX-6bit on Your PC

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

Refer to the action plan below to initialize the model.

The download manager will automatically pull several gigabytes of data.

The installer diagnoses your environment to deploy the most compatible profile.

🔍 Hash-sum: d335e3dcdb965aa202c4537d21bf92fb | 🕓 Last update: 2026-06-26



  • Processor: high single-core performance needed for token latency
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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