Cell-Akamai: Auditable Inference Evaluation

Cell-Akamai makes AI-agent inference auditable on Akamai Cloud. Our prototype runs against the workshop Qwen3-4B vLLM endpoint, measures server-reported tokens/s and latency under concurrent sessions, preserves failures, and commits SHA-256 answer and dataset evidence before independent scoring. A sanitized dashboard supports live walkthroughs while durable, serialized experiments continue. We demonstrate lossless original/FCO/FCG reconstruction for frozen synthetic ECA fixtures and retain negative results. Completed measurements are short pilots; hour-scale validation is ongoing, and we claim neither training nor quality improvement. Source and verified evidence: https://github.com/biobitworks/cell-akamai/pull/12

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// Project brief

// Built with

AkamaiPython, vLLM, Qwen3-4B, Jupyter, Kubernetes, pytest, SHA-256/Merkle receipts, JSONL, local dashboard

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