Launch gemma-4-E4B-it-MLX-8bit PC with NPU

Running this model locally is fastest when deployed through Docker.

Please follow the instructions listed below to get started.

After cloning, fire up the application using Docker.

📦 Hash-sum → 3badecb8e00a0449fd686e486cbb941c | 📌 Updated on 2026-06-22 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: enough space for background apps and OS overhead Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

The gemma-4-E4B-it-MLX-8bit model is a compact yet powerful language model designed for efficient inference on consumer hardware. Built on the MLX framework, it leverages a 4‑billion‑parameter transformer architecture optimized for low‑latency tasks while maintaining high contextual understanding. By employing 8‑bit integer quantization, the model reduces memory footprint and enables smooth deployment on devices with limited resources. Benchmarks show competitive perplexity scores and fast generation speeds, making it suitable for real‑time chatbots, content creation, and edge AI applications. Open‑source releases include model cards, conversion scripts, and integration examples, encouraging collaboration and further optimization by the research community.

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