gemma-4-26B-A4B-it-qat-GGUF Locally via Ollama 2 Zero Config Windows

gemma-4-26B-A4B-it-qat-GGUF Locally via Ollama 2 Zero Config Windows

To get this model running locally in no time, utilize the built-in WSL tools.

Use the instructions provided below to complete the setup.

The download manager will automatically pull several gigabytes of data.

The installer will automatically analyze your hardware and select the optimal configuration.

🧮 Hash-code: 076c032ba0a9238798e8cbfd7038054c • 📆 2026-06-29



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk: high-speed SSD 120 GB to cache model layers
  • 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.

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