If you want the fastest local installation for this model, use Docker.

Make sure to follow the instructions below.

1-click setup: the app automatically fetches the large weight files.

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

🗂 Hash: 6af18edc1e739ac751d582c3abb5c8a4 • Last Updated: 2026-06-23 Verify CPU: multi-threading optimized for fast prompt processing RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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