查看購物車
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2141121

推論成長對人工智慧硬體和伺服器基礎設施的影響

The Impact of Inference Growth on AI Hardware & Server Infrastructure
出版日期: | 出版商: ABI Research | 英文 16 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

具體益處：

  • 我們將根據預期推理處理成長的領域，調整我們的人工智慧 (AI) 基礎設施和加速器藍圖，並支援採用靈活的硬體而不是專用硬體。
  • 根據工作負載規模、運轉率、延遲要求以及功耗和記憶體限制，我們優先考慮最具吸引力的商業推理晶片機會。
  • 我們制定夥伴關係、收購、產品認證和系統整合策略，以因應日益多元化的人工智慧基礎設施市場。

重要問題的答案：

  • 到 2035 年，生產推理的興起將如何改變對人工智慧處理器和伺服器基礎設施的需求？
  • 哪些工作負載特性會提高專用推理硬體的商業性可行性？靈活加速器何時仍將是首選？
  • 隨著人工智慧基礎設施變得越來越異構，商用推理晶片將在哪個領域抓住最大的商業機會？

調查要點：

  • 對 2035 年前的 AI 雲端容量和 2031 年前的 AI 處理器出貨量進行預測分析（包括訓練和推理工作負載的相對成長率）。
  • 評估與預填、解碼、記憶體、網路、編配和使用率相關的需求如何改變處理器架構和基礎設施採購。
  • 對通用、客製化和專用推理晶片策略進行比較評估，包括夥伴關係、收購和異質部署模式如何影響市場進入和差異化。

本報告的主要受眾：

  • 負責人工智慧基礎設施、產品和策略的雲端供應商、託管推理平台、新雲端和模型提供者正在規劃未來的訓練和推理能力。
  • 半導體和加速器供應商的產品策略、公司策略、業務發展和市場調查團隊正在評估推理矽的商業機會和市場進入途徑。
  • 在伺服器OEM（原始設備製造商）公司擔任產品經理、系統架構師和基礎設施策略團隊成員。該職位涉及確定要支援的加速器架構，以及如何透過整合、部署和生命週期服務實現差異化。

目錄

第1章 主要發現

第2章主要預測

第3章：主要公司和生態系統

  • NVIDIA
  • AMD
  • INTEL
  • CEREBRAS
  • SAMBANOVA
  • QUALCOMM
  • MEDIATEK

第4章：推論成長將如何改變硬體採購

第5章：商業機會與建議

  • 雲端提供者和託管推理提供者
  • 半導體供應商
  • 伺服器 OEM 製造商
簡介目錄
Product Code: AN-6611

Actionable Benefits:

  • Align Artificial Intelligence (AI) infrastructure and accelerator roadmaps with where inference growth is most likely to support flexible versus specialized hardware deployment.
  • Prioritize the most attractive merchant inference silicon opportunities based on workload scale, utilization, latency requirements, and power and memory constraints.
  • Shape partnership, acquisition, product qualification, and system-integration strategies for an increasingly heterogeneous AI infrastructure market.

Critical Questions Answered:

  • How will the shift toward production inference change demand for AI processors and server infrastructure through 2035?
  • Which workload characteristics make specialized inference hardware commercially viable, and when will flexible accelerators remain the preferred choice?
  • Where will merchant inference silicon capture the strongest commercial opportunities as AI infrastructure becomes more heterogeneous?

Research Highlights:

  • Forecast analysis of AI cloud capacity through 2035 and AI processor shipments through 2031, including the relative growth of training and inference workloads.
  • Assessment of how prefill, decode, memory, networking, orchestration, and utilization requirements are changing processor architecture and infrastructure procurement.
  • Comparative assessment of merchant, custom, and specialized inference silicon strategies, including how partnerships, acquisitions, and heterogeneous deployment models are shaping market entry and differentiation.

Who Should Read This?

  • AI infrastructure, product, and strategy leaders at cloud providers, managed inference platforms, neoclouds, and model providers planning future training and inference capacity.
  • Product strategy, corporate strategy, business development, and market intelligence teams at semiconductor and accelerator vendors evaluating inference silicon opportunities and routes to market.
  • Server Original Equipment Manufacturer (OEM) product leaders, system architects, and infrastructure strategy teams deciding which accelerator architectures to support and how to differentiate through integration, deployment, and lifecycle services.

TABLE OF CONTENTS

1 KEY FINDINGS

2 KEY FORECASTS

3 KEY COMPANIES AND ECOSYSTEMS

  • 3.1 NVIDIA
  • 3.2 AMD
  • 3.3 INTEL
  • 3.4 CEREBRAS
  • 3.5 SAMBANOVA
  • 3.6 QUALCOMM
  • 3.7 MEDIATEK

4 WHERE INFERENCE GROWTH CHANGES HARDWARE BUYING

5 COMMERCIAL OPPORTUNITIES AND RECOMMENDATIONS

  • 5.1 CLOUD PROVIDERS AND MANAGED INFERENCE PROVIDERS
  • 5.2 SILICON VENDORS
  • 5.3 SERVER OEMS