Zero-Click Run gemma-4-26B-A4B-it-qat-GGUF on AMD/Nvidia GPU

The most efficient approach for a local installation is leveraging Docker containers.

Follow the step-by-step instructions below.

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

During setup, the script automatically determines and applies the best settings.

🧾 Hash-sum — 263761ac63648e12e4197c231b086ceb • 🗓 Updated on: 2026-07-02 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: enough space for background apps and OS overhead Disk Space:70 GB free space for full FP16 weights storage Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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.

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