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Automatic Model-Hardware Co-Adaptation for Heterogeneous AI Accelerators
Large language models now evolve faster than production inference systems can be ported and optimized. New releases change attention, MoE routing, quantization formats, KV-cache layout, and parallel execution patterns, while deployed accelerator fleets remain heterogeneous across hardware generation
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