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ApexFlow AI is a unified quantum-classical hybrid stream gateway designed for token-efficient routing and self-healing telemetry ingestion. Track 1 (Hybrid Token-Efficient Routing Agent): Deploys an 11-qubit Variational Quantum Classifier (VQC) dispatcher trained on the IBM Heron r2 QPU, paired with a 3-tier Google Gemma 4 ensemble on AMD MI300X GPUs. Tasks are classified by the VQC and dispatched to the optimal model tier: Tier 1: 33× parallel Gemma 4 E4B instances for sentiment/NER/summary (0 Fireworks tokens via local GPU inference) Tier 2: 10× Gemma 4 26B A4B instances for factual/math/logic (0 Fireworks tokens) Tier 3: 8× Gemma 4 31B instances for code generation/debugging (0 Fireworks tokens) Each tier loads dynamically into MI300X VRAM at 92% utilization, runs parallel consensus voting, then unloads before loading the next tier. Tasks failing Gemma consensus are referred to Fireworks AI's all-model API for the cheapest correct answer. The architecture exploits AMD's 192GB HBM3 memory for massive parallel ensemble inference while keeping the Docker image under 300MB through runtime model downloads from HuggingFace. Track 3 (Unicorn Gateway): Self-healing schema reconciliation pipeline for real-time streaming data (F1 telemetry, weather, financial markets). Features VQC router → Levenshtein/Regex/BERT/Gemma healing pipeline with live glassmorphic dashboard. Validated on LUMI supercomputer.
13 Jul 2026