Using the Windows Package Manager is the quickest way to trigger the setup.

Use the instructions provided below to complete the setup.

The script takes care of fetching the multi-gigabyte model weights.

There is no manual tuning required; the builder deploys the best matching configuration.

🔗 SHA sum: c55a13c20a01e8e7e193c4d684d04f41 | Updated: 2026-06-30 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: enough space for background apps and OS overhead Disk: 150+ GB for high-context vector database storage GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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