How to Run gemma-4-26B-A4B-it-qat-GGUF Easy Build

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How to Run gemma-4-26B-A4B-it-qat-GGUF Easy Build

The fastest method for installing this model locally is by using Docker.

Refer to the instructions below to proceed.

Next, run the Docker command to spin up the container.

📘 Build Hash: 830ead67313b3de3a76ec31353a5c43b • 🗓 2026-06-27
  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • 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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