If you need a near-instant local setup, just fetch files via a basic curl request.
Check out the detailed setup guide below to begin.
The setup auto-downloads all needed files (several GBs).
Once launched, the wizard detects your specs to configure the model for maximum efficiency.
The Gemma-4-26B-A4B-it-AWQ-4bit model leverages a 26‑billion parameter architecture built on the A4B transformer design, delivering strong performance on both reasoning and generation tasks. It employs AWQ quantization to achieve efficient 4‑bit inference while preserving accuracy across a wide range of benchmarks. The model supports instruction‑following with a context window that enables complex multi‑step problem solving. Compared to its predecessors, it shows a notable improvement in reasoning speed and memory footprint without sacrificing fluency. A
| Spec | Value |
|---|---|
| Parameter Count | 26 B |
| Quantization | AWQ 4‑bit |
| Latency (typical) | ~120 ms |
can be used to present key specs such as parameter count, quantization method, and typical latency. Developers can integrate this model into production pipelines using standard inference frameworks, benefiting from its balanced trade‑off between size and capability.
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