Deploying locally takes the least amount of time when executed through native OS tools.
Use the instructions provided below to complete the setup.
The engine will automatically fetch large dependencies in the background.
To save you time, the system will automatically determine efficient resource allocation.
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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