gemma-4-26B-A4B-it-qat-GGUF Using Pinokio Full Method

For an instant local deployment, running a pre-configured shell script is ideal.

Kindly follow the on-screen instructions below.

The download manager will automatically pull several gigabytes of data.

The automated script takes care of everything, tailoring the setup to your specs.

🔒 Hash checksum: 167993f39f20d360ccc0d2b8cecea12b • 📆 Last updated: 2026-06-24
  • CPU: 8-core / 16-thread recommended for orchestration
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Storage: extra room for future model updates and datasets
  • GPU: high memory bandwidth GPU for next-gen local AI pipeline

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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