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OmniTide: Co-Designing Algorithms and Systems for Efficient On-Device Omni-LLM Streaming
On-device streaming omni-modal inference safeguards user privacy and eliminates prohibitive per-token API costs, but faces a critical bottleneck: the continuous influx of multimodal data rapidly exhausts constrained memory and compute budgets via monotonic KV cache growth. Existing sparse attention
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