Dr Dhruv Sharma

Zero-Click Run gemma-4-26B-A4B-it-GGUF For Low VRAM (6GB/8GB) Windows

Zero-Click Run gemma-4-26B-A4B-it-GGUF For Low VRAM (6GB/8GB) Windows

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

Follow the guidelines below to continue.

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

The deployment tool scans your environment and automatically chooses the ideal parameters for your OS.

💾 File hash: d2f547e77cc4e5e2a339c4475640d1c7 (Update date: 2026-06-23)



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Disk Space:70 GB free space for full FP16 weights storage
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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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