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Design LLM routing for regulated AI avatars using eligibility gates, model-choice evidence, fallbacks and deployment-aware evaluation.
Learn when speculative decoding improves a real-time AI avatar, how to benchmark draft-and-verify inference and when the extra model is not worth it.
Design GPU scheduling for real-time AI avatars across placement, partitioning, batching, priorities and model residency on private infrastructure.
Build synthetic conversation probes for real-time AI avatars across speech, knowledge, actions, voice and media—without relying on customer content.
Design AI turn-taking across VAD, endpointing, interruption and action controls so real-time avatars respond quickly without speaking over users.
Measure AI inference energy consumption across GPUs, complete servers and cooling, then compare power per successful avatar conversation.
