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юни 28, 2026 In Finetunes / No Comments
How to Run gemma-4-E4B-it-MLX-8bit Locally via Ollama 2



Using Docker is the absolute quickest way to install this model on your local machine.





Use the instructions provided below to complete the setup.





Then, run the build command to initialize the Docker container.



📎 HASH: d06296850a00b59a60c50d1d1dd6a25e | Updated: 2026-06-23


  • Processor: next-gen chip for heavy context processing
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Storage: extra room for future model updates and datasets
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)
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.
Parameters4 B
Quantization8‑bit integer
FrameworkMLX
Release typeOpen‑source
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