gemma-4-26B-A4B-it-qat-GGUF Quantized GGUF For Beginners Windows

gemma-4-26B-A4B-it-qat-GGUF Quantized GGUF For Beginners Windows

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

Follow the guidelines below to continue.

The setup auto-streams the model assets (expect a multi-GB download).

The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.

🔐 Hash sum: 0d277bdbeff6dd1ff231d954f1288335 | 📅 Last update: 2026-06-22



  • Processor: high single-core performance needed for token latency
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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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