
The fastest way to get this model running locally is via Docker.
Follow the guidelines below to continue. The system automatically triggers a cloud download for all heavy weights.
Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.
📦 Hash-sum → 5ebd8bbb3cb290b27a1bf1821845920f | 📌 Updated on 2026-06-23
- Processor: Intel i7 / Ryzen 7 for heavy Quantized models
- RAM: enough space for background apps and OS overhead
- Disk Space: required: fast PCIe 4.0 drive for instant boots
- GPU: high memory bandwidth GPU for next-gen local AI pipeline
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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.
| Parameters |
4 B |
| Quantization |
5‑bit |
| Framework |
MLX |
| Inference Type |
IT (Interactive) |
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