How to Deploy gemma-4-26B-A4B-it-NVFP4 Full Method

How to Deploy gemma-4-26B-A4B-it-NVFP4 Full Method

Running this model locally is fastest when deployed through Docker.

Please follow the instructions listed below to get started.

Next, start the model by running the docker-compose command.

🗂 Hash: dfd67dfc6728d8595ff918bf958f3a8bLast Updated: 2026-06-27



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: enough space for background apps and OS overhead
  • Disk Space: 100 GB for multi-modal model vision components
  • Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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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