gemma-4-E4B-it Windows 10 with Native FP4 Local Guide

Publié le 28/06/26

gemma-4-E4B-it Windows 10 with Native FP4 Local Guide

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

Please follow the instructions listed below to get started.

Next, start the model by running the docker-compose command.

📄 Hash Value: 9347e08e40cb9e2390b070310f66093d | 📆 Update: 2026-06-22



  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: required: 16 GB absolute minimum for small models
  • Disk Space: 100 GB for multi-modal model vision components
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

The gemma-4-E4B-it model represents a significant advancement in open‑source language models, combining massive scale with efficient inference capabilities. It features 2.5 trillion parameters, enabling it to understand and generate highly nuanced text across a wide range of domains. With a context window of 128K tokens, the model can maintain coherence in long‑form conversations and documents. A dedicated

can illustrate key technical specifications:

Parameters 2.5 trillion
Context Length 128K tokens
Training Data web‑scale corpus (2023‑2024)
Inference Speed > 100 tokens/sec on GPU

Benchmarks show that gemma-4-E4B-it outperforms previous models on reasoning, coding, and multilingual tasks while consuming less computational resources.

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