gemma-4-26B-A4B-it-qat-GGUF 100% Private PC No-Internet Version Local Guide Windows

To install this model locally in the shortest time, opt for Docker.

Refer to the instructions below to proceed.

The system automatically triggers a cloud download for all heavy weights.

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

🔐 Hash sum: c89cc7c6db8a6619c782c1ea78abe4ec | 📅 Last update: 2026-06-23



  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk Space:70 GB free space for full FP16 weights storage
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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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