1.0Witology Market Trend Research Institutehttps://witology.com.tw/en智璞https://witology.com.tw/en/post/author/witology/七月_AI晶片專題｜百家爭鳴的邊緣之AI推論晶片趨勢解析(上) - 智璞產業趨勢研究所rich600338<blockquote class="wp-embedded-content" data-secret="J3LtvcyZIV"><a href="https://witology.com.tw/en/post/6820/">July_AI Chip Topics｜AI Inference Chip Trend Analysis by the Edge of Hundreds of Controversies(Up)</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://witology.com.tw/en/post/6820/embed/#?secret=J3LtvcyZIV" width="600" height="338" title=""July_AI Chip Topics｜AI Inference Chip Trend Analysis at the Edge of Hundreds of Contests (Upper)" - Wisdom Industry Trend Research Institute" data-secret="J3LtvcyZIV" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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</script>https://witology.com.tw/wp-content/uploads/2025/08/1-1.jpg1170540如前所述，未來若要讓 AI "Agent" 真正大規模普及，單靠「大型模型」並不足以支撐，還必須整合雲端、邊緣與終端等不同層級的運算能力。LLM 一旦上線，推論成本往往會超過訓練成本，單日推論規模可能高達數億個 "token"，此時晶片的運算效率與能耗表現便會被放大檢視。