Running this model locally is fastest when deployed through Docker.
Use the instructions provided below to complete the setup.
The client handles the setup, pulling gigabytes of data automatically.
The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.
đ Hash sum: b5cbf619a808297d037d1d47d1f782bb | đ
Last update: 2026-06-23 - Processor: 6-core 3.5 GHz minimum required
- RAM: 64 GB to avoid OOM crashes on large contexts
- Disk Space: 100 GB for multi-modal model vision components
- GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference
|
The
gemma-4-26B-A4B-it-GGUF model represents a
state-of-the-art addition to the Gemma family, built on a
26âbillion parameter architecture optimized for both reasoning and generation tasks. It leverages an
enhanced attention mechanism that allows the model to capture longer-range dependencies, achieving a
context window of 128K tokens for complex prompts. The model is quantized in
GGUF format, delivering
significantly lower memory footprint while preserving nearâoriginal performance across a range of benchmarks. In comparative testing,
gemma-4-26B-A4B-it-GGUF outperforms its predecessors on reasoning challenges, scoring
84.3% accuracy on multiâstep problem solving. Its openâsource nature and efficient inference make it suitable for deployment in production environments, research projects, and edge devices where
computational resources are constrained. | Parameters | 26 billion |
| Context length | 128K tokens |
| Quantization | GGUF |
| Benchmark accuracy | 84.3% |
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