For an instant local deployment, running a pre-configured shell script is ideal.
Go through the configuration rules shown below.
All large files and heavy weights are downloaded automatically by the script.
There is no manual tuning required; the builder deploys the best matching configuration.
The Gemma-4-26B-A4B-it-AWQ-4bit model leverages a 26鈥慴illion 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鈥慴it inference while preserving accuracy across a wide range of benchmarks. The model supports instruction鈥慺ollowing with a context window that enables complex multi鈥憇tep 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鈥疊 |
| Quantization | AWQ 4鈥慴it |
| Latency (typical) | ~120鈥痬s |
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鈥憃ff between size and capability.
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