6G部署場景：從簡單的無線設備升級到分散式人工智慧基礎設施

本報告研究了 6G 部署場景，提供了 6G 部署路徑的場景分析、6G 無線設備的收入預測以及按運營商類型分類的決策矩陣。

實際益處：

• 基於對延遲敏感的工作負載、資料主權和企業人工智慧需求，我們可以確定電信業者可以在人工智慧價值鏈中建立競爭優勢的領域。
• 透過比較連接提供者、AI 所有者、AI 基礎設施供應商和 AI 部署推動者的策略，您可以確定電信業中每個層級最現實的角色。
• 這將有助於明確 2026 年至 2028 年基礎設施投資決策的優先事項，這將影響 2030 年代 6G 的商業部署、配備 AI 晶片組的無線設備的推出以及分散式 AI 的貨幣化。

主要問題解答：

• 電信業者應該將 6G 主要視為對其無線存取網路的升級，還是應該儘早投資於分散式 AI 基礎設施所需的核心網、邊緣網、運算和編配能力？
• 面對已經掌控雲端平台、人工智慧模式、開發者生態系統和企業關係的超大規模資料中心業者，電信業者如何在人工智慧領域建立永續的競爭優勢？
• 從簡單的 6G RAN 升級到 AI 網格和分散式 AI 基礎架構模型，將如何影響營運、架構和資本支出 (CAPEX)？

研究亮點：

• 基於場景的 6G 部署路徑分析：以分階段 RAN 改進為中心的簡單無線升級，以及需要轉型為分散式 AI 基礎架構的 AI 網格模型。
• 2029 年至 2034 年 6G 無線設備收入預測：配備 AI 晶片組的設備在 6G 無線設備總收入中的佔有率預計將從 23% 上升至 86%。
• 將電信業者類型對應到四種人工智慧策略的決策矩陣：連接供應商、人工智慧房東、人工智慧基礎設施供應商和人工智慧部署推動者。

目錄

主要發現

市場展望

市場機會與威脅

電信業者可以進入的領域

• 電信營運商決策矩陣

主要預測

6G升級場景分析

• 簡易無線接取網路(RAN) 升級
• AI Grid 分散式 AI 基礎設施

6G核心網路中的人工智慧

主要公司生態系統

關於人工智慧網格的思考

Actionable Benefits:

• Identify where telco operators can create a competitive advantage in the Artificial Intelligence (AI) value chain based on latency-sensitive workloads, sovereignty, and enterprise AI demand.
• Compare connectivity provider, AI landlord, AI infrastructure provider, and AI application enabler strategies to determine the most realistic role for different telco tiers.
• Prioritize 2026 to 2028 infrastructure decisions that will shape operator positioning for 6G commercial deployments, AI-chipset radio adoption, and distributed AI monetization in the 2030s.

Critical Questions Answered:

• Should operators approach 6G primarily as a radio access upgrade, or invest early in the core, edge, compute, and orchestration capabilities required for distributed AI infrastructure?
• Where can telcos create defensible AI value against hyperscalers that already control cloud platforms, AI models, developer ecosystems, and enterprise relationships?
• What are the operational, architectural, and Capital Expenditure (CAPEX) implications of moving from a simple 6G Radio Access Network (RAN) upgrade to an AI-grid and distributed AI infrastructure model?

Research Highlights:

• Scenario-based analysis of two 6G deployment paths: a simple radio upgrade built around incremental RAN improvements and an AI-grid model requiring distributed AI infrastructure transformation.
• Forecasts of 6G radio revenue and 6G radios with AI chipset revenue from 2029 to 2034, including the rise of AI-chipset radios from 23% to 86% of total 6G radio revenue.
• Decision matrix mapping telco operator types to four AI strategies: connectivity provider, AI landlord, AI infrastructure provider, and AI application enabler.

Who Should Read This?

• Chief Technology Officers (CTOs) and network investment at mobile operators deciding whether 6G should be deployed as an incremental RAN upgrade or used as a platform for AI infrastructure expansion.
• Tier One operators planning AI-RAN, AI-native core evolution, Graphics Processing Unit (GPU)-accelerated compute, and distributed inference infrastructure for the 2029 to 2030 6G deployment window.
• Tier Two and Tier Three operators assessing whether to pursue connectivity, AI landlord, targeted edge hosting, or partnership-led strategies instead of competing directly with hyperscalers in AI infrastructure ownership.

TABLE OF CONTENTS

Key Findings

Market Outlook

Market Opportunities Threats

Where Can Telcos Play

• Decision Matrix for Telco Operators

Key Forecasts

6G Upgrade Scenario Analysis

• Simple Radio Access Network RAN Upgrade
• AI Grid Distributed AI Infrastructure

AI in the 6G Core Network

Key Companies Ecosystem

AI-Grid Discussion