Deploying this model locally is quickest when done via Docker.

Make sure to follow the instructions below.

After that, launch the environment using docker-compose.

📊 File Hash: 8cb7edb3839e2123a1f96db4c14a88ee — Last update: 2026-06-25 Verify Processor: 6-core 3.5 GHz minimum required RAM: minimum 16 GB for stable 8B model loading Disk: 150+ GB for high-context vector database storage Graphics: CUDA Compute Capability 8.0+ required for flash-attention

The **gemma-4-E4B-it-MLX-5bit** model represents a compact yet powerful addition to the Gemma family, optimized for on-device inference. Built on a 4‑billion parameter architecture, it leverages MLX optimizations to deliver high throughput while maintaining a minimal footprint. By employing 5‑bit quantization, the model achieves a favorable balance between accuracy and memory usage, making it suitable for resource‑constrained environments. Inference is tailored for interactive tasks, providing real‑time responses with reduced latency compared to larger counterparts. The design incorporates advanced routing mechanisms that enhance contextual understanding without sacrificing speed. Overall, the **gemma-4-E4B-it-MLX-5bit** offers a compelling solution for developers seeking efficient AI capabilities in edge deployments.

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