Adapting a 128-Expert MoE Model to AWS Inferentia2 Reveals Expert Weighting Issues
gemma inference
| Source: Dev.to | Original article
AI model ported to AWS Inferentia2 reveals expert weighting issue.
Porting a 128-expert Mixture of Experts (MoE) model, specifically the Gemma-4 26B-A4B, to AWS Inferentia2 has encountered significant challenges. The model's complex architecture, including a dual-path feed-forward network and a sparse expert loop, has made the porting process difficult. A notable issue arose where every rank weighted the wrong experts, despite the CPU reference being perfect and all unit tests passing.
This development matters because the Gemma-4 26B-A4B model offers a compelling balance between performance and cost. With only 4 billion active parameters, it achieves near 31B quality while dramatically reducing inference costs per token. Successful deployment on AWS Inferentia2 could further optimize costs for sustained traffic, making it an attractive option for production environments.
As the community continues to work on resolving the porting issues, the next steps will be crucial. Developers will be watching for updates on the correction of the expert weighting issue and the successful deployment of the Gemma-4 26B-A4B model on AWS Inferentia2. This will likely involve recompilation and potential adjustments to the model's architecture to ensure seamless integration with the Inferentia2 chips.
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