HBM在AI GPU的應用：市佔率分析、產業趨勢與統計、成長預測（2026-2031）

據 Mordor Intelligence 稱，用於 AI GPU 的 HBM 市場規模預計將從 2025 年的 88.8 億美元成長到 2026 年的 125.6 億美元，到 2031 年達到 468.2 億美元，預計 2026 年至 2031 年的複合年成長率為 30.10%。

本報告按 HBM 代數（HBM2e、HBM3、HBM3e、HBM4）、儲存容量（最高 32 GB、32–64 GB、64–128 GB 及以上）、應用領域（訓練 GPU、推理 GPU、超大規模資料中心業者計算和科學國防、邊緣人工智慧）、最終用戶（美國標量、企業級細分政府和人工智慧）。市場預測以美元計價。

全球人工智慧GPU中HBM的發展趨勢與洞察

AI GPU 的記憶體頻寬瓶頸正在推動 HBM 的搭載率。

AI GPU 的 HBM 市場正在擴張。這是因為，在現代大規模語言模型的推理過程中，記憶體傳輸往往成為瓶頸，而非單純的計算吞吐量。 NVIDIA 宣布，GB300 Blackwell Ultra 整合了八個 12 層 HBM3e 堆疊，每個 GPU 的容量達到 288GB，吞吐量達到 8TB/s。這表明，隨著每一代新加速器的推出，記憶體容量都在不斷提升。這項設計變更意義重大，因為它是一項架構上的變革，而非臨時性的調整。這意味著，即使 AI GPU 市場中 HBM 的出貨模式發生變化，每個新產品週期中 HBM 的容量仍將持續成長。隨著推理上下文的延長，對鍵值快取的需求也隨之增加；隨著模型上下文視窗的擴大，低容量配置將不再適用於運作環境中的推理。這使得頻寬限制成為供應商永續的收入來源，因為 HBM 已成為 AI GPU 市場中必不可少的設計元素，而非高階選項。因此，隨著 AI 硬體向高容量和高堆疊配置發展，記憶體堆疊在加速器組件配置中的戰略重要性不斷增加。

快速採用HBM3e作為下一代訓練叢集

由於HBM3e相比HBM3擁有明顯的頻寬優勢，它迅速佔據主導，並成為尖端訓練平台的標準。 NVIDIA發布了配備141GB HBM3e和4.8TB/s記憶體頻寬的H200，這有助於大規模訓練部署樹立了新的效能標準。谷歌也討論了配備192GB HBM3e和7300GB/s頻寬的Ironwood TPU，而AWS發布了配備144GB HBM3e和4.9TB/s頻寬的Trainium3。這表明，超大規模資料中心業者的客製化晶片正在推動不同平台上的記憶體標準趨於一致。這對AI GPU市場的HBM具有重大意義，因為對HBM的需求不再僅僅依賴NVIDIA的出貨量，而是得到了更廣泛的加速器計畫的支持。 SK海力士表示，其在2025會計年度創紀錄的業績主要得益於對AI內存的需求，並強調訓練集群的快速部署如何帶動了HBM銷量的成長。更廣泛地說，HBM3e將在一個週期內成為最先進AI基礎設施的最低實用規格，這將加速供應商的運轉率，並保持AI GPU的HBM市場快速成長。

HBM堆疊和中介層整合高成本

由於堆疊式記憶體成本高昂，以及頂級加速器所需的先進封裝技術，用於人工智慧GPU的HBM市場仍面臨著巨大的限制。本文指出，HBM的成本仍遠高於傳統DRAM，而且當在單一晶片上增加多個堆疊層和複雜的中介層封裝時，成本還會進一步上升。這對用於人工智慧GPU的HBM市場造成了不均衡的影響，因為超大規模超大規模資料中心業者比預算固定的中小企業、學術機構和公共部門專案更容易承受更高的採購成本。此外，記憶體和封裝成本的上漲會影響系統到達最終用戶之前的加速器定價，因此這種壓力會波及整個價值鏈。因此，儘管技術需求明確且效能顯著提升，但除最高價值工作負載外，其他工作負載的採用速度正在放緩。在成本曲線改善之前，用於人工智慧GPU的HBM市場仍將是那些能夠透過大規模訓練和運作推理的規模經濟效益來證明高價系統部署合理性的買家最容易進入的市場。

細分市場分析

到2025年，HBM3e將佔到58.67%的銷售額，成為目前平台週期中AI GPU HBM市場中佔比最大的一代。其主導地位歸功於NVIDIA H200、Blackwell B200和Google Ironwood TPU的廣泛應用，這些產品都提高了AI硬體記憶體的最低標準。 HBM3仍然透過H100的持續生產維持著一定的市場需求，而HBM2e則仍然局限於較老的高效能運算（HPC）和對成本敏感的科學運算工作負載，不再是AI GPU HBM產業的核心。此細分市場的結構也受到認證規則的影響，因為JEDEC互通性標準導致工程樣品和實際量產收入之間存在時間差。與其他許多半導體類別相比，這種時間差使得代際更替更具可預測性，因為客戶會更早簽訂契約，而供應商則需要更長的檢驗週期。在這種模式下，HBM3e受益於其作為首個廣泛應用的、能夠同時滿足大規模訓練叢集和更苛刻的推理系統內存需求的代際產品。因此，HBM3e不僅在2025年成為營收支柱，也成為傳統HBM3部署環境與HBM4首次商業部署之間的橋樑。由此可見，HBM3e不僅取代了HBM3，也重新定義了整個AI GPU HBM市場的基本規範。

HBM4預計將成為成長最快的記憶體技術，直至2031年，使其成為該領域未來最重要的成長要素。報告指出，HBM4將於2026年正式進入市場，屆時三大供應商都將在同年獲得NVIDIA Vera Rubin平台的認證，隨後平台將開始量產出貨。 SK海力士也宣布，已於2026年6月提前交付了12層HBM4E樣品，三星也於2026年5月下旬交付了HBM4E樣品。這表明開發週期正在迅速縮短。這種速度意義重大，因為下一代記憶體－用於AI GPU的HBM的市場規模正受到各代認證成本回收期縮短以及市場加速向高級產品轉型的影響。此外，該領域的定價模式也呈現出一種規律，即新一代記憶體的平均售價較高，直到製造流程成本的降低顯現出來。因此，對於能夠儘早獲得認證並高效擴大生產規模的供應商而言，HBM4 具有提升銷售量和最佳化產品組合的潛力。這意味著 AI GPU 的 HBM 市場將圍繞著快速的世代更替展開，市場主導將很大程度上取決於認證的時機以及純粹的產能。這也意味著客戶越來越傾向於不僅根據當前的引進週期，而且根據未來的發展藍圖來規劃採購。

2025年，64-128 GB頻寬的銷售額佔比達到48.34%，在基準年佔據了AI GPU HBM市場規模的核心地位。 141 GB H200等系統以及接近下一容量等級邊界的平台支撐了這一地位，維持了該容量範圍內的商業性覆蓋。隨著舊款HBM2e設計被更高密度的後續產品取代，32 GB及以下容量段的重要性持續下降，但32-64 GB容量段在中等規模的推理和邊緣高效能運算（HPC）應用場景中仍然佔據重要地位，這些場景目前尚不需要頂級記憶體池。此細分市場的主要驅動力是模型託管和訓練需求不斷成長，促使買家轉向更高容量的配置。從實際角度來看，高記憶體容量不再只是一項高階功能，而是正在成為大型模型高效能運作的基本要求。這種轉變已經影響到設備的更換週期，雲端服務供應商正利用從基於 H100 的節點升級到 H200 來改善推理延遲和處理能力。此外，隨著記憶體容量與所應用運算資源的商業性效用直接掛鉤，人工智慧 GPU 中 HBM 市場的需求趨勢也發生了變化。因此，容量分類不僅反映價格範圍，也越來越反映工作負載的複雜性。

預計到2031年，頻寬超過128GB的產品將實現最快成長，成為AI GPU HBM市場中最具戰略意義的容量層級。 NVIDIA推出了單GPU容量為288GB的“GB300 Blackwell Ultra”，Google也發布了容量同樣為288GB的“TPU 8t”，這表明主要廠商正在向同一高容量範圍靠攏。 NVIDIA也推出了“GB300 NVL72”，該產品在72個GPU上整合了總合37TB的HBM3e記憶體。這表明機架式設計現在圍繞著非常大規模的聚合記憶體池構建。這對於AI GPU的HBM市場至關重要，因為市佔率正在向高密度配置轉移，這些配置具有更高的單系統記憶體容量和更強的定價能力。向12層堆疊的轉變也在推動這種配置轉變，因為這些配置更加複雜，並且繼續將價值集中在高容量硬體上。因此，隨著需求轉向更高容量級別，營收成長速度將超過銷售成長速度。這將鞏固人工智慧GPU中HBM市場的高階細分領域，並提升能夠在高堆疊密度下維持良率的供應商的重要性。此外，隨著部署基礎轉向記憶體密集型推理和訓練系統，這也為長期維持高溢價奠定了基礎。

區域分析

2025年，北美佔了AI GPU HBM市場的52.43%佔有率，是營收貢獻最大的區域市場。該地區受益於美國集中了最大的超大規模資料中心業者營運商、領先的AI GPU設計公司以及眾多尖端模型開發公司。這種組合在硬體設計、雲端部署和終端用戶需求之間建立了緊密的聯繫，鞏固了該地區在AI GPU HBM市場整體中的穩固地位。 2026年，美國的出口政策也影響了該地區的需求模式。美國工業與安全局（BIS）確認，即使位於中國境外，D組5國家營業單位也需遵守先進運算設備的許可要求。這縮小了可使用尖端系統的客戶群，並將合規的出貨機會轉移到國內和盟國市場。 NVIDIA也宣布，微軟、Oracle雲端基礎設施和CoreWeave正在部署GB300 NVL72系統，這為Blackwell Ultra平台的短期出貨前景提供了支援。因此，該地區不僅在大規模採購方面仍然處於中心地位，而且在影響 HBM 平台在 AI GPU 市場其他領域的部署時機方面仍然處於中心地位。

亞太地區預計將在2026年至2031年間成為成長最快的地區，這主要得益於其主導的供應端地位以及該地區不斷成長的計算投資。韓國仍然是主要的HBM製造地，而台灣在先進封裝流程方面發揮著至關重要的作用，因此該地區已成為AI GPU的HBM市場的核心。這種供應端地位意義重大，因為該地區的企業能夠影響整個市場的認證速度、分配決策以及大規模生產過渡的時間。同時，在韓國、日本和印度政府和超大規模資料中心業者的支持下，蓬勃發展的AI基礎設施建設正在創造出口導向供應和本地需求。這種組合使亞太地區有別於北美，因為它既是AI GPU的HBM市場的製造中心，也是新興的應用目的地。此外，這意味著區域政策、資本投資和技術藍圖可以同時影響供應量和終端市場吸收能力。因此，亞太地區的成長願景不僅僅是“迎頭趕上”，而是體現了其作為全球健康米供應鏈核心營運中心的角色。

到2025年，歐洲將佔據相當大的市場佔有率，但佔有率相對較小。德國、英國和法國將成為人工智慧基礎設施部署的關鍵區域中心。由於採購週期較長，且合規性要求往往優先於大規模硬體生產，歐洲的成長速度較慢，導致其人工智慧GPU用HBM市場的成長率低於北美和亞太地區。南美和中東及非洲的貢獻仍處於起步階段，但鑑於中東地區國家主導的計算項目，這些市場在預測期後半段的佔有率可能會更大。因此，區域組成仍然不均衡，人工智慧GPU用HBM市場仍集中在那些兼具高階運算需求、平台存取和與半導體供應鏈緊密聯繫的地區。

其他好處：

• Excel格式的市場預測（ME）表
• 3個月的分析師支持

目錄

第1章：引言

• 研究假設和市場定義
• 調查範圍

第2章：調查方法

第3章執行摘要

第4章 市場狀況

• 市場概覽
• 宏觀經濟因素對市場的影響
• 市場促進因素
  • AI 和 GPU 中的記憶體頻寬瓶頸正在推動 HBM 的普及。
  • 擴大先進封裝的產能將提高 HBM 的產量。
  • 快速採用HBM3e作為下一代訓練叢集
  • 超大規模資料中心業者的客製化加速器計畫正在推動 HBM 的採用率提高。
  • 多晶片GPU架構的興起使得每個加速器可安裝的記憶體量增加。
  • HBM4藍圖正在加速認證和供應合約的簽訂。
• 市場限制因素
  • CoWoS和其他先進封裝技術的產能有限。
  • HBM堆疊和中介層整合高成本
  • 高堆疊DRAM組件良率下降
  • 人工智慧GPU的出口限制和客戶集中風險。
• 產業供應鏈分析
• 監理情勢
• 技術展望
• 波特五力分析

第5章 市場規模與成長預測

• 按世代分類的 HBM
  • HBM2e
  • HBM3
  • HBM3e
  • HBM4
• 按儲存容量
  • 32 GB 或更少
  • 32～64 GB
  • 64～128 GB
  • 128 GB 或更多
• 透過使用
  • 訓練 GPU
  • 用於推理的GPU
  • 高效能運算和科學人工智慧
  • 邊緣人工智慧
• 最終用戶
  • 超大規模資料中心業者和雲端服務供應商
  • 企業人工智慧部署狀態
  • 研究與超級計算中心
  • 政府/國防
• 地區
  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 韓國
    • 印度
    • 東南亞
    • 其他亞太國家
  • 南美洲
  • 中東和非洲

第6章 競爭情勢

• 市場集中度
• 策略趨勢
• 市佔率分析
• 公司簡介
  • SK hynix Inc.
  • Samsung Electronics Co., Ltd.
  • Micron Technology, Inc.
• Other Ecosystem Players
  • NVIDIA Corporation
  • Advanced Micro Devices, Inc.
  • Intel Corporation
  • Taiwan Semiconductor Manufacturing Company Limited
  • ASE Technology Holding Co., Ltd.
  • Amkor Technology, Inc.
  • Tokyo Electron Limited
  • Applied Materials, Inc.
  • Lam Research Corporation
  • Kioxia Holdings Corporation
  • SanDisk Corporation
  • Renesas Electronics Corporation
  • Qualcomm Incorporated
  • Broadcom Inc.
  • Marvell Technology, Inc.
  • Apple Inc.
  • Alibaba Group Holding Limited
  • Alphabet Inc.
  • Amazon.com, Inc.
  • Microsoft Corporation
  • Meta Platforms, Inc.

第7章 市場機會與未來展望

According to Mordor Intelligence, the HBM for AI GPUs market size is expected to increase from USD 8.88 billion in 2025 to USD 12.56 billion in 2026 and reach USD 46.82 billion by 2031, growing at a CAGR of 30.10% over 2026-2031.

This report is Segmented by Generation (HBM2e, HBM3, Hbm3e, and HBM4), Memory Capacity (Up To 32 GB, 32-64 GB, 64-128 GB, and More), Application (Training GPUs, Inference GPUs, HPC and Scientific AI, and Edge AI), End User (Hyperscalers, Enterprise AI, Research and Supercomputing, and Government and Defense), and Geography (Asia-Pacific, and More). The Market Forecasts are Provided in Terms of Value (USD).

Global HBM For AI GPUs Market Trends and Insights

AI GPU Memory Bandwidth Bottlenecks Raising HBM Attach Rates

The HBM for AI GPUs market is moving higher because modern large language model inference is often limited by memory movement rather than raw compute throughput. NVIDIA stated that the GB300 Blackwell Ultra integrates eight 12-high HBM3e stacks, delivering 288GB and 8TB/s per GPU, demonstrating how memory capacity is rising with each new accelerator generation. That design change matters because it is architectural rather than temporary, which means higher HBM content remains tied to each new product cycle, even if unit shipment patterns shift across HBM for the AI GPU market. Longer reasoning contexts also raise key-value cache requirements, so lower-capacity configurations become less suitable for production inference as model context windows expand. This makes bandwidth limits a durable revenue driver for suppliers, as HBM becomes a required design element rather than a premium option in the AI GPU market. The result is that the memory stack keeps gaining strategic weight in accelerator bill of materials as AI hardware moves toward higher-capacity and taller-stack configurations.

Rapid HBM3e Adoption In Next-Generation Training Clusters

HBM3e quickly moved into the lead because it offered a clear bandwidth advantage over HBM3 and became the standard on the most advanced training platforms. NVIDIA listed the H200 with 141GB of HBM3e and 4.8 TB/s of memory bandwidth, which helped define the new performance floor for large-scale training deployments. Google also described Ironwood TPU with 192GB of HBM3e and 7,300GB/s, while AWS presented Trainium3 with 144GB of HBM3e and 4.9TB/s, showing that hyperscaler custom silicon is reinforcing the same memory standard across competing platforms. This matters for the HBM for AI GPU market because HBM demand no longer depends solely on NVIDIA shipments and instead draws support from a broader set of accelerator programs. SK hynix said its FY2025 record results were driven by AI memory demand, underscoring how quickly training-cluster deployment translated into HBM revenue expansion. The wider implication is that HBM3e became the minimum practical specification for frontier AI infrastructure within a single cycle, accelerating supplier utilization and keeping HBM for the AI GPU market on a steep growth path.

High Cost Of HBM Stacks And Interposer Integration

The HBM for the AI GPU market still faces a meaningful brake from the cost of stacked memory and the advanced packaging needed to make it usable in top-end accelerators. The input material makes clear that HBM remains far more expensive than conventional DRAM, and that the cost rises further when several stacks and complex interposer packaging are added to a single chip. This has an uneven effect across the HBM for AI GPUs market because hyperscalers can absorb higher procurement costs more easily than smaller enterprises, academic buyers, and public-sector programs with fixed budgets. The pressure also flows through the supply chain, as higher memory and packaging costs affect accelerator pricing before systems reach end users. That slows adoption outside the highest-value workloads, even when technical demand is clear, and performance gains are strong. Until cost curves improve, the HBM for AI GPUs market will remain most accessible to buyers who can justify premium systems through large-scale training or production inference economics.

Other drivers and restraints analyzed in the detailed report include:

1. Advanced Packaging Capacity Expansion Unlocking HBM Output
2. HBM4 Roadmap Pulling Forward Qualification And Supply Agreements
3. Limited CoWoS And Other Advanced-Packaging Capacity

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

HBM3e accounted for 58.67% of revenue in 2025, making it the largest generation in the HBM for the AI GPU market during the current platform cycle. Its lead came from broad deployment across the NVIDIA H200, Blackwell B200, and Google Ironwood TPU, all of which set a higher memory threshold for competitive AI hardware. HBM3 still retained residual demand through ongoing H100 production, while HBM2e remained tied to older HPC and cost-sensitive scientific computing workloads that no longer define the center of the HBM for AI GPUs industry. The structure of this segment is also shaped by qualification rules because JEDEC interoperability standards create a lag between engineering samples and meaningful volume revenue. That lag brings more predictability to generational handovers than in many other semiconductor categories because customers commit earlier and suppliers need a longer validation window. Within that pattern, HBM3e benefited from being the first broadly available generation that aligned with the memory needs of both large training clusters and more demanding inference systems. It therefore served as both the revenue anchor of 2025 and the bridge between legacy HBM3 deployments and the first HBM4 commercial ramps. The result is that HBM3e did not simply replace HBM3, and instead reset the baseline specification across the HBM for AI GPU market.

HBM4 is projected to be the fastest-growing generation through 2031, and that makes it the most important forward driver inside this segment. The input states that HBM4 entered the revenue mix in 2026 with volume shipments tied to NVIDIA Vera Rubin, after all 3 major suppliers qualified for the platform in the same year. SK hynix also said it shipped 12-layer HBM4E samples in June 2026 ahead of its earlier timeline, while Samsung supplied HBM4E samples in late May 2026, which shows how quickly development cycles are compressing. That speed matters because the HBM for AI GPUs market size for next-generation memory is being shaped by shorter recovery windows for each generation's qualification costs and by faster migration toward premium products. The segment also reflects a pricing pattern in which newer memory generations hold elevated average selling prices before cost learning spreads through manufacturing. HBM4 therefore carries both volume growth and mix improvement potential for suppliers that qualify early and scale efficiently. This keeps the HBM for AI GPU market centered on a rapid generational ladder, where leadership depends on qualification timing as much as on raw manufacturing capacity. It also means customers are increasingly planning procurement around future roadmaps instead of only current deployment cycles.

The 64-128 GB tier held a 48.34% revenue share in 2025, which placed it at the center of the HBM for the AI GPU market size during the base year. That position was supported by systems such as the H200 at 141GB and by platforms close to the boundary of the next capacity band, which kept this range commercially broad. The up to 32 GB segment continued to lose importance as older HBM2e designs gave way to denser successors, while the 32-64 GB range remained relevant for mid-tier inference and edge HPC use cases that did not yet require top-end memory pools. The main force in this segment is that model hosting and training requirements are steadily pushing buyers toward larger configurations. In practical terms, higher memory capacity is no longer just a premium feature; it is becoming a baseline requirement for strong performance on larger models. That shift has already influenced replacement cycles, as cloud operators have used H200 upgrades to improve inference latency and capacity compared with H100-based nodes. It also changed the demand profile in the HBM for AI GPUs market, as memory capacity now tracks directly with the commercial usefulness of deployed compute. For that reason, capacity segmentation increasingly mirrors workload complexity instead of price brackets alone.

The above 128 GB band is expected to record the fastest growth through 2031 and is becoming the most strategic capacity tier in the HBM for AI GPUs market. NVIDIA described the GB300 Blackwell Ultra with 288GB per GPU, while Google presented TPU 8t around the same 288GB class, confirming that major vendors are converging on the same high-capacity bracket. NVIDIA also highlighted GB300 NVL72 with 37TB of total HBM3e across 72 GPUs, which shows how rack-scale design is now built around very large aggregate memory pools. This matters because the HBM for AI GPUs market share is shifting toward denser configurations that carry higher per-system memory content and stronger pricing power. The move to 12-high stacks also supports that mix shift because these builds are more complex and keep value concentrated in higher-capacity hardware. Revenue therefore grows faster than unit volume when demand migrates toward the top of the capacity ladder. This reinforces the premium end of the HBM for AI GPU market and raises the importance of suppliers that can maintain yields on taller stacks. It also supports a longer runway for premium pricing as the installed base moves toward memory-heavy inference and training systems.

Complete Report Scope:

• By HBM Generation
  • HBM2e
  • HBM3
  • HBM3e
  • HBM4
• By Memory Capacity
  • Up to 32 GB
  • 32-64 GB
  • 64-128 GB
  • Above 128 GB
• By Application
  • Training GPUs
  • Inference GPUs
  • HPC and Scientific AI
  • Edge AI
• By End User
  • Hyperscalers and Cloud Service Providers
  • Enterprise AI Deployments
  • Research and Supercomputing Centers
  • Government and Defense
• Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America accounted for 52.43% of the HBM for AI GPUs market in 2025, making it the largest regional contributor by revenue. The region benefits from the concentration of the largest hyperscalers, the leading AI GPU designer, and many of the most advanced model developers in the United States. That combination creates a tight link between hardware design, cloud deployment, and end demand, which keeps the regional base strong across the HBM-based AI GPU market. U.S. export policy also shaped regional demand patterns in 2026, as BIS confirmed that license requirements for advanced computing items extend to entities headquartered in Country Group D:5, even when they are outside China. This narrowed the pool of accessible customers for the most advanced systems and pushed more compliant shipment opportunities toward domestic and allied-country demand. NVIDIA also stated that Microsoft, Oracle Cloud Infrastructure, and CoreWeave are deploying GB300 NVL72 systems, which support near-term shipment visibility tied to Blackwell Ultra platforms. The region, therefore, remains central not only because it buys large volumes, but also because it shapes the timing of platform adoption across the rest of the HBM for the AI GPU market.

Asia Pacific is projected to be the fastest-growing region over 2026-2031, driven by a mix of supply leadership and rising regional compute investment. The region already sits close to the production core of the HBM for the AI GPUs market because South Korea remains the main HBM manufacturing base, and Taiwan remains essential in advanced packaging flows. That supply position matters because regional companies influence qualification pace, allocation decisions, and generational ramp timing across the whole market. At the same time, the input shows growing sovereign and hyperscaler-backed AI infrastructure activity across South Korea, Japan, and India, which adds local demand on top of export-oriented supply. This combination makes Asia Pacific different from North America because it participates heavily on both sides of the HBM for AI GPUs market, as a manufacturing anchor and as a rising deployment destination. It also means that regional policy, capital spending, and technology roadmaps can simultaneously affect both volume availability and end-market absorption. For that reason, Asia Pacific's growth profile is broader than a simple catch-up story and reflects its role as a core operating base for the global HBM chain.

Europe held a meaningful but smaller share in 2025, with Germany, the United Kingdom, and France serving as the main regional centers for AI infrastructure deployment in the input material. The region moved more slowly because procurement cycles are longer and compliance priorities have often preceded large hardware ramps, which kept growth below North America and Asia Pacific in the HBM for AI GPUs market. South America, the Middle East, and Africa remained early-stage contributors, though sovereign compute programs in the Middle East suggest these markets could gain greater weight later in the forecast period. The geographic mix, therefore, remains uneven, with the HBM for the AI GPU market size still concentrated in regions that combine high-end compute demand, platform access, and strong links to the semiconductor supply chain.

1. SK hynix Inc.
2. Samsung Electronics Co., Ltd.
3. Micron Technology, Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Impact of Macroeconomic Factors on the Market
• 4.3 Market Drivers
  • 4.3.1 AI GPU Memory Bandwidth Bottlenecks Raising HBM Attach Rates
  • 4.3.2 Advanced Packaging Capacity Expansion Unlocking HBM Output
  • 4.3.3 Rapid HBM3e Adoption in Next-Generation Training Clusters
  • 4.3.4 Hyperscaler Custom Accelerator Programs Increasing HBM Design Wins
  • 4.3.5 Rising Multi-Die GPU Architectures Increasing Per-Accelerator Memory Content
  • 4.3.6 HBM4 Roadmap Pulling Forward Qualification and Supply Agreements
• 4.4 Market Restraints
  • 4.4.1 Limited CoWoS and Other Advanced-Packaging Capacity
  • 4.4.2 High Cost of HBM Stacks and Interposer Integration
  • 4.4.3 Yield Losses in High-Stack DRAM Assembly
  • 4.4.4 Export Controls and Customer Concentration Risk in AI GPUs
• 4.5 Industry Supply Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By HBM Generation
  • 5.1.1 HBM2e
  • 5.1.2 HBM3
  • 5.1.3 HBM3e
  • 5.1.4 HBM4
• 5.2 By Memory Capacity
  • 5.2.1 Up to 32 GB
  • 5.2.2 32-64 GB
  • 5.2.3 64-128 GB
  • 5.2.4 Above 128 GB
• 5.3 By Application
  • 5.3.1 Training GPUs
  • 5.3.2 Inference GPUs
  • 5.3.3 HPC and Scientific AI
  • 5.3.4 Edge AI
• 5.4 By End User
  • 5.4.1 Hyperscalers and Cloud Service Providers
  • 5.4.2 Enterprise AI Deployments
  • 5.4.3 Research and Supercomputing Centers
  • 5.4.4 Government and Defense
• 5.5 Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 South Korea
    • 5.5.3.4 India
    • 5.5.3.5 Southeast Asia
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 South America
  • 5.5.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Products and Services, Recent Developments)
  • 6.4.1 SK hynix Inc.
  • 6.4.2 Samsung Electronics Co., Ltd.
  • 6.4.3 Micron Technology, Inc.
• 6.5 Other Ecosystem Players
  • 6.5.1 NVIDIA Corporation
  • 6.5.2 Advanced Micro Devices, Inc.
  • 6.5.3 Intel Corporation
  • 6.5.4 Taiwan Semiconductor Manufacturing Company Limited
  • 6.5.5 ASE Technology Holding Co., Ltd.
  • 6.5.6 Amkor Technology, Inc.
  • 6.5.7 Tokyo Electron Limited
  • 6.5.8 Applied Materials, Inc.
  • 6.5.9 Lam Research Corporation
  • 6.5.10 Kioxia Holdings Corporation
  • 6.5.11 SanDisk Corporation
  • 6.5.12 Renesas Electronics Corporation
  • 6.5.13 Qualcomm Incorporated
  • 6.5.14 Broadcom Inc.
  • 6.5.15 Marvell Technology, Inc.
  • 6.5.16 Apple Inc.
  • 6.5.17 Alibaba Group Holding Limited
  • 6.5.18 Alphabet Inc.
  • 6.5.19 Amazon.com, Inc.
  • 6.5.20 Microsoft Corporation
  • 6.5.21 Meta Platforms, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-space and Unmet-Need Assessment