How to Run gemma-4-E4B-it-MLX-8bit on Copilot+ PC with Native FP4

The fastest method for installing this model locally is by using Docker.

Follow the sequence of steps detailed below.

1-click setup: the app automatically fetches the large weight files.

To guarantee smooth performance, the installation process auto-selects the best possible options for your PC.

🔒 Hash checksum: f0afdaf8f7bd686d841df8cacec8d955 • 📆 Last updated: 2026-06-28 Verify Processor: next-gen chip for heavy context processing RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: free: 80 GB on system drive for scratch space Graphics: 12 GB VRAM minimum required for basic quantization

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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