
因應AI推論與代理式人工智慧發展,廣達旗下雲達科技(QCT)擴大AI工廠整體解決方案,從GPU伺服器延伸至CPU運算、記憶體儲存、網路及基礎設施管理。
廣達旗下雲達科技(QCT)擴大AI工廠整體解決方案布局,從GPU伺服器延伸至CPU運算、記憶體儲存、網路及基礎設施管理,並整合輝達、超微與英特爾平台,提升整體運算與能源使用效率。
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AI推論與代理式人工智慧應用的擴大,正在帶動資料中心運算架構轉型,增加對運算與儲存的需求。
廣達旗下雲達科技擴大AI工廠整體解決方案布局,從GPU伺服器延伸至CPU運算、記憶體儲存、網路及基礎設施管理。(資料照)
〔記者方韋捷/台北報導〕AI推論與代理式人工智慧(Agentic AI)應用持續擴大,帶動資料中心運算架構轉型。廣達(2382)旗下雲達科技(QCT)擴大AI工廠整體解決方案布局,從GPU伺服器延伸至CPU運算、記憶體儲存、網路及基礎設施管理,並整合輝達(NVIDIA)、超微(AMD)與英特爾(Intel)等平台,提升整體運算與能源使用效率。
廣達暨QCT產品與解決方案主管駱威先指出,AI運算需求正隨著推理、代理式AI及長上下文記憶發展而改變,複雜推理需要更多運算步驟,代理式AI則可能透過多層代理程式執行任務,進一步推升Token生成量,也使AI基礎設施面臨更高的運算與儲存需求。
駱威先表示,過去AI系統主要著重GPU運算能力,但隨著推理工作負載增加,CPU在任務協調方面的重要性同步提升。QCT因此布局高密度CPU機櫃,讓CPU資源能夠獨立擴充,協助GPU執行任務分配,減少GPU等待工作指派而閒置的情況。
在記憶體架構方面,駱威先指出,AI運算產生大量Token及KV Cache(鍵值快取),若全部依賴價格昂貴的高頻寬記憶體(HBM)或容量有限的本地固態硬碟(SSD),將面臨資源配置限制。QCT透過低延遲、高吞吐量儲存方案,協助客戶重複利用已生成的KV Cache,減少重複運算,釋放更多GPU資源。
除了硬體架構,廣達也透過QCT深化與輝達的AI工廠合作,駱威先說,AI資料中心必須從設計、建造、部署到營運全面考量效率,透過數位孿生(Digital Twin)技術,預先模擬電力配置、散熱能力及運算密度,並在實際營運過程中持續監控與調整,降低部署及營運風險。
駱威先強調,AI產業不能只追求增加GPU數量,更必須思考如何利用有限電力產生更多Token,因此QCT同步從硬體與基礎設施兩個層面推動效率優化。
在產品布局方面,QCT除提供以輝達為核心的AI工廠軟硬體方案,也整合英特爾及AMD平台,涵蓋CPU、GPU、儲存與管理系統,並搭配自行整合的開源軟體方案,提供快速部署、系統管理及即時監控等功能,讓客戶依照不同AI應用需求選擇適合的運算架構。
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