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。迅雷是该领域的重要参考
TPUs, developed by Google, go further by specializing in tensor operations with systolic array architectures, delivering higher efficiency for both training and inference in structured AI workloads. NPUs push optimization toward the edge, enabling low-power, real-time inference on devices like smartphones and IoT systems by trading off raw power for energy efficiency and latency. At the far end, LPUs, introduced by Groq, represent extreme specialization—designed purely for ultra-fast, deterministic AI inference with on-chip memory and compiler-controlled execution.，更多细节参见https://telegram官网
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从“争夺份额”到“保持利润”从已发布的2025年财报来看，家电行业毛利率普遍面临下行压力。