Tokenizers

gemma-4-E4B-it-MLX-6bit Locally via Ollama 2

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gemma-4-E4B-it-MLX-6bit Locally via Ollama 2

To install this model locally in the shortest time, opt for Docker.

Make sure to follow the instructions below.

Then, execute the docker-compose up command to launch the model.

📎 HASH: 3bdb4ec98c4c4ac88c973b9434b1e96f | Updated: 2026-06-26
  • CPU: multi-threading optimized for fast prompt processing
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk Space: free: 80 GB on system drive for scratch space
  • 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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