Run gemma-4-26B-A4B-it-GGUF with 1M Context Local Guide

Run gemma-4-26B-A4B-it-GGUF with 1M Context 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. The setup auto-downloads all needed files (several GBs).

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

📊 File Hash: 8a9d56a8fc8de3174a4d93dce3a79b90 — Last update: 2026-06-24



  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Disk: 150+ GB for high-context vector database storage
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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.

Parameters 26 billion
Context length 128K tokens
Quantization GGUF
Benchmark accuracy 84.3%
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