Interoperability Testing Framework for AI-RAN Multi-Vendor Deployments
AI-RAN Alliance Testing WG
AI-RAN Alliance
Abstract
The AI-RAN Alliance presents a comprehensive interoperability testing framework for AI models deployed across multi-vendor radio access networks. The framework defines 42 test cases covering model portability, inference latency, data interface compatibility, and performance validation across different vendor equipment. Initial testing across 5 vendor implementations reveals that while basic AI model deployment succeeds in 85% of cases, real-time inference performance varies by up to 40% between vendors, highlighting the need for standardized AI execution environments.
AI Summary
- Interoperability testing framework for AI-RAN with 42 test cases.
- Tests model portability, latency, data interfaces, and performance.
- 85% basic deployment success across 5 vendor implementations.
- Up to 40% inference performance variation between vendors.
Key Findings
- 1Model format standardization (ONNX) resolves most portability issues.
- 2Data interface differences are the primary cause of interoperability failures.
- 3Inference latency variations stem from different hardware acceleration approaches.
Industry Implications
Standardized AI execution environments are critical for multi-vendor AI-RAN.
The framework provides a basis for AI-RAN certification programs.
Operators can use these test cases to validate AI-RAN interoperability.
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