Deploy gemma-4-26B-A4B-it-qat-GGUF Locally via Ollama 2 Local Guide

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Deploy gemma-4-26B-A4B-it-qat-GGUF Locally via Ollama 2 Local Guide

Using Docker is the absolute quickest way to install this model on your local machine.

Simply follow the directions outlined below.

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

🗂 Hash: c499fbd0b0c2d9add02b9c7c920dff80 • Last Updated: 2026-06-26
  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: required: 16 GB absolute minimum for small models
  • Storage: extra room for future model updates and datasets
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

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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