gemma-4-26B-A4B-it-qat-GGUF on AMD/Nvidia GPU No Admin Rights

The fastest way to get this model running locally is via Docker.

Review and follow the instructions below.

The installer automatically pulls the model (could be multiple GBs).

During setup, the script automatically determines and applies the best settings tailored to your machine.

📎 HASH: 11ffea1044c2eadb025d4701e3041518 | Updated: 2026-06-28



  • CPU: modern architecture (Zen 3 / Alder Lake minimum)
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Storage:100 GB free space for HuggingFace cache folder
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

gemma-4-26B-A4B-it-qat-GGUF is a large language model built on the Gemma architecture with 26 billion parameters. It employs *QAT* techniques to improve inference efficiency while maintaining high performance. The model offers an 8K token context window, enabling detailed reasoning and long‑form generation. Benchmarks demonstrate *competitive* results across multilingual tasks, especially in code generation and factual QA. Its GGUF format ensures broad compatibility with inference engines and reduces memory usage for deployment.

Parameters 26 B
Context Length 8K tokens
Quantization QAT (GGUF)
Architecture Gemma‑4
Primary Use Text generation, code, QA
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