gemma-4-26B-A4B-it-GGUF Windows 11 Local Guide

To install this model locally in the shortest time, opt for Docker.

Follow the step-by-step instructions below.

The setup auto-streams the model assets (expect a multi-GB download).

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

📎 HASH: 195aeaa661aa630946cedd0b28d45a09 | Updated: 2026-06-25



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk Space: free: 80 GB on system drive for scratch space
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

The gemma-4-26B-A4B-it-GGUF model represents a state-of-the-art addition to the Gemma family, built on a 26‑billion parameter architecture optimized for both reasoning and generation tasks. It leverages an enhanced attention mechanism that allows the model to capture longer-range dependencies, achieving a context window of 128K tokens for complex prompts. The model is quantized in GGUF format, delivering significantly lower memory footprint while preserving near‑original performance across a range of benchmarks. In comparative testing, gemma-4-26B-A4B-it-GGUF outperforms its predecessors on reasoning challenges, scoring 84.3% accuracy on multi‑step problem solving. Its open‑source nature and efficient inference make it suitable for deployment in production environments, research projects, and edge devices where computational resources are constrained.

Parameters26 billion
Context length128K tokens
QuantizationGGUF
Benchmark accuracy84.3%
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