AI晶片供應鏈韌性策略評估-以NVIDIA為例

Abstract

2023 年，H100 GPU 的等待時間一度超過 52 週，全球雲端大廠競相採購，卻難以即時取得貨源，當時的瓶頸並非來自輝達的晶片設計能力，而是先進封裝產能無法跟上需求爆發。台積電 CoWoS 月產能約為 1.3 萬片，難以支撐生成式 AI 帶來的算力需求急速擴張，與此同時，美國出口管制持續收緊，迫使輝達不斷調整產品規格與市場配置，當需求成長與政策限制同時發生，供應鏈韌性已不再只是後端的成本管理問題，而是直接影響輝達能否穩定出貨、維持市場主導地位的關鍵能力。 本研究以2022至2025年輝達AI晶片供應鏈為個案，採質性個案研究法，輔以自建供應鏈韌性評估框架（SCRA），從「地理集中度」、「供應商替代性」與「技術壟斷性」三個維度，系統評估晶圓製造、先進封裝（CoWoS）、HBM記憶體與系統組裝等關鍵節點的韌性策略與改善幅度。研究分析輝達如何在需求急速擴張與政策環境持續變動的雙重壓力下，透過產能鎖定、多供應商布局與技術路線調整，推動供應鏈由高度集中走向有限度分散。 跨時間點的節點評估結果顯示，各類韌性策略的改善幅度與節點技術特性高度相關：在技術壁壘相對較低、供應商替代性較高的環節，韌性已有明顯改善；但在晶圓製造與CoWoS前段等技術門檻最高的節點，短期內可行的結構調整仍相當有限。本研究據此歸納出輝達韌性提升的結構性路徑，並提出後續在供應鏈配置與政策風險管理上的研究方向與實務啟示。

In 2023, lead times for NVIDIA's H100 GPU extended beyond 52 weeks as hyperscalers competed for supply they could not secure. The constraint was not chip design, but packaging Ssigns and market reconfigurations. Together, these pressures elevated supply chain resilience from a back-office concern to the central variable shaping NVIDIA's ability to ship at scale and hold its market position. This study examines NVIDIA's AI chip supply chain from 2022 to 2025 using a qualitative case study approach, supplemented by a self-constructed Supply Chain Resilience Assessment framework (SCRA) across three dimensions: geographic concentration, supplier substitutability, and technological monopolization. The study analyzes how NVIDIA pursued structural diversification through capacity locking, multi-supplier arrangements, and technology roadmap adjustments across key nodes including wafer fabrication, advanced packaging, HBM memory, and system assembly. Findings show that resilience improvements were inversely correlated with technological barriers. Back-end packaging and system assembly saw meaningful gains, while front-end wafer fabrication and CoWoS remained highly concentrated in Taiwan through 2025, with limited structural change. The study identifies structural pathways for resilience improvement and outlines directions for future research.