Run gemma-4-26B-A4B-it with Native FP4
Deploying this model locally is quickest when done via Docker.
Follow the step-by-step instructions below.
After cloning, fire up the application using Docker.
The gemma-4-26B-A4B-it model represents a significant advancement in open?source language models, combining a massive 26?billion parameter architecture with optimized inference performance. It leverages an attention?sparse design that reduces computational load while maintaining high fidelity in both factual and creative tasks. The model supports a 2048?token context window and incorporates a refined instruction?tuning pipeline that improves alignment with user intent. A comparison with peer models shows superior scores in reasoning, code generation, and multilingual understanding, as summarized below.
| Metric | Value |
|---|---|
| Parameters | 26?B |
| Context Length | 2048 tokens |
| Training Data | Web?scale multilingual corpus |
| Inference Speed | ~120?tokens/s on GPU |
Users can integrate the model into production environments via standard APIs, benefiting from its balanced trade?off between size, speed, and capability.
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