查看購物車
  • Simplified Chinese
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1896165

人工智慧推理晶片市場預測至2032年：按晶片類型、部署方式、應用領域、最終用戶和地區分類的全球分析

AI Inference Chips Market Forecasts to 2032 - Global Analysis By Chip Type, Deployment, Application, End User, and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球人工智慧推理晶片市場價值將達到 510 億美元，到 2032 年將達到 2,276 億美元，預測期內複合年成長率為 23.8%。

人工智慧推理晶片是專門設計的處理器，能夠高效運行訓練好的人工智慧模型，用於即時決策和資料處理。這些晶片針對低延遲、高吞吐量和高能源效率進行了最佳化，使其適用於邊緣設備、自主系統、智慧攝影機和資料中心。它們的日益普及正在推動醫療保健、汽車、零售和工業自動化等行業的可擴展人工智慧部署。

根據 LinkedIn 的趨勢，針對自動駕駛和智慧監控等即時任務進行推理最佳化的晶片的擴展，正在推動工業 4.0 各個領域的更廣泛應用。

快速部署邊緣人工智慧應用

邊緣人工智慧應用的快速部署推動了對推理晶片的需求，這些晶片能夠實現更靠近資料來源的低延遲處理。從智慧攝影機和工業IoT設備到自動駕駛汽車，邊緣人工智慧都需要專為即時決策而最佳化的專用晶片。這一趨勢降低了對雲端基礎設施的依賴，增強了隱私保護，並提高了回應速度。隨著各行業採用邊緣運算，推理晶片已成為可擴展、分散式人工智慧生態系統的關鍵基礎，從而推動了全球市場成長。

高昂的開發和檢驗成本

開發人工智慧推理晶片涉及複雜的架構、先進的封裝和嚴格的檢驗流程。高昂的研發成本，加上昂貴的製造和測試要求，構成了巨大的進入門檻。確保與各種人工智慧框架和工作負載的兼容性進一步增加了開發成本。這些資本密集要求使得中小企業難以與老牌半導體巨頭競爭。因此，儘管對人工智慧加速發展的需求日益成長，但高成本仍然是阻礙其廣泛應用的主要因素。

自主系統和智慧基礎設施的擴展

自主系統和智慧基礎設施的擴展為人工智慧推理晶片創造了巨大的發展機會。自動駕駛汽車、無人機和機器人依賴即時推理來實現導航、安全和決策。同樣，智慧城市和互聯基礎設施也需要能夠高效處理海量感測器資料的晶片。隨著政府和企業加大對自動化和數位轉型的投入，推理晶片有望在交通、能源和城市環境中實現智慧自適應系統，從而獲得顯著成長。

利用通用處理器提升人工智慧效能

通用處理器（包括CPU和GPU）的進步對專用推理晶片構成了威脅。隨著主流處理器整合AI加速功能，某些應用對專用推理硬體的需求下降。這種融合趨勢對推理晶片的差異化構成了挑戰，尤其是在對成本敏感的市場。如果通用處理器持續提升大規模AI效能，可能會削弱對小眾推理解決方案的需求，迫使專業供應商加快創新步伐以保持競爭力。

新冠疫情的感染疾病

新冠疫情擾亂了半導體供應鏈，導致人工智慧推理晶片的生產延遲和成本上升。然而，疫情也加速了數位化進程，推動了對人工智慧醫療、遠端監控和自動化解決方案的需求。疫情期間，推理晶片在醫療成像、診斷支援和智慧設備領域獲得了廣泛應用。疫情後的復甦階段，企業加大了對彈性供應鏈和本地化製造的投資。疫情也凸顯了推理晶片在關鍵產業實現自適應資料驅動型解決方案的重要性。

預計在預測期內，圖形處理器（GPU）細分市場將佔據最大的市場佔有率。

由於其多功能性和平行處理能力，圖形處理器 (GPU) 預計將在預測期內佔據最大的市場佔有率。 GPU 可加速深度學習模型，對訓練和推理任務都至關重要。其在雲端、邊緣和企業環境中的可擴展性確保了其廣泛應用。隨著人工智慧應用在各行各業的擴展，GPU 將繼續成為推理運算的基礎，在預測期內保持最大的市場佔有率，並鞏固其作為人工智慧工作負載主要驅動力的地位。

預計在預測期內，雲端細分市場將實現最高的複合年成長率。

受人工智慧即服務（AIaaS）平台日益普及的推動，預計雲端細分市場在預測期內將實現最高成長率。企業越來越依賴雲端基礎架構來部署可擴展的推理工作負載，而無需投資昂貴的本地硬體。雲端服務供應商正在整合專用推理晶片，以提供更快、更有效率的人工智慧服務。對靈活且經濟高效的人工智慧解決方案日益成長的需求將推動雲端推理的成長，使其成為人工智慧推理晶片市場中成長最快的細分市場。

比最大的地區

預計亞太地區將在整個預測期內保持最大的市場佔有率。這主要得益於該地區強大的半導體製造基礎，以及中國、日本、韓國和台灣地區人工智慧技術的快速發展。該地區正受益於對人工智慧驅動型產業（例如家電、汽車和智慧基礎設施）的大力投資。政府主導的各項措施以及不斷擴大的研發中心進一步鞏固了亞太地區的主導地位。隨著對邊緣人工智慧和雲端服務需求的成長，該地區正逐步成為推理晶片的重要中心。

預計年複合成長率最高的地區

在預測期內，北美地區預計將呈現最高的複合年成長率，這主要得益於人工智慧、雲端運算和國防領域的強勁需求。眾多大型科技公司和半導體創新企業的存在，推動了推理晶片的快速普及。政府對人工智慧研究的資助以及國內晶片製造舉措，也將進一步促進市場成長。隨著企業在醫療保健、金融和自動駕駛系統等領域擴大人工智慧的應用，北美有望成為人工智慧推理晶片市場成長最快的地區。

免費客製化服務

購買此報告的客戶可享有以下免費自訂選項之一：

  • 公司概況
    • 最多三家新增市場參與企業進行全面分析
    • 主要參與企業（最多3家公司）的SWOT分析
  • 區域細分
    • 根據客戶要求，對主要國家進行市場估算和預測，並計算複合年成長率（註：可行性需確認）。
  • 競爭基準化分析
    • 從產品系列、地域覆蓋範圍和策略聯盟等方面對主要參與企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 摘要
  • 相關利益者
  • 調查範圍
  • 調查方法
  • 研究材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球人工智慧推理晶片市場（按晶片類型分類）

  • 專用積體電路（ASIC）
  • 圖形處理器（GPU）
  • 中央處理器（CPU）
  • 神經處理單元
  • 現場可程式閘陣列
  • 混合人工智慧晶片

6. 全球人工智慧推理晶片市場（以部署方式分類）

  • 基於雲端的
  • 邊緣設備
  • 本地資料中心
  • 嵌入式系統
  • 行動平台
  • 分散式人工智慧系統

第7章 全球人工智慧推理晶片市場（按應用分類）

  • 電腦視覺
  • 自然語言處理
  • 語音辨識
  • 自主系統
  • 建議引擎
  • 預測分析

第8章 全球人工智慧推理晶片市場（按最終用戶分類）

  • 科技公司
  • OEM
  • 醫療保健提供者
  • 製造業
  • 零售與電子商務
  • 政府和國防機構

9. 全球人工智慧推理晶片市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第10章：重大進展

  • 協議、夥伴關係、合作和合資企業
  • 併購
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第11章 企業概況

  • NVIDIA Corporation
  • Intel Corporation
  • Advanced Micro Devices
  • Qualcomm Incorporated
  • Google LLC
  • Amazon Web Services
  • Microsoft Corporation
  • Apple Inc.
  • Huawei Technologies
  • MediaTek Inc.
  • Graphcore Ltd.
  • Cerebras Systems
  • Groq Inc.
  • Mythic AI
  • Hailo Technologies
  • Ambarella Inc.
Product Code: SMRC32853

According to Stratistics MRC, the Global AI Inference Chips Market is accounted for $51.0 billion in 2025 and is expected to reach $227.6 billion by 2032 growing at a CAGR of 23.8% during the forecast period. AI Inference Chips are specialized processors designed to efficiently execute trained artificial intelligence models for real-time decision-making and data processing. These chips are optimized for low latency, high throughput, and energy efficiency, making them suitable for edge devices, autonomous systems, smart cameras, and data centers. Their growing adoption supports scalable AI deployment across industries such as healthcare, automotive, retail, and industrial automation.

According to LinkedIn trends, expansion of inference-optimized chips for real-time tasks like autonomous driving and smart surveillance is strengthening adoption across Industry 4.0 sectors.

Market Dynamics:

Driver:

Rapid deployment of edge AI applications

The rapid deployment of edge AI applications is fueling demand for inference chips that deliver low-latency processing closer to data sources. From smart cameras and industrial IoT devices to autonomous vehicles, edge AI requires specialized chips optimized for real-time decision-making. This trend reduces reliance on cloud infrastructure, enhances privacy, and improves responsiveness. As industries embrace edge computing, inference chips are becoming critical enablers of scalable, decentralized AI ecosystems, driving strong market growth worldwide.

Restraint:

High development and validation costs

Developing AI inference chips involves complex architectures, advanced packaging, and rigorous validation processes. High R&D costs, coupled with expensive fabrication and testing requirements, create significant barriers to entry. Ensuring compatibility with diverse AI frameworks and workloads further adds to development expenses. Smaller firms struggle to compete with established semiconductor giants due to these capital-intensive demands. As a result, high costs remain a key restraint, slowing broader adoption despite the growing need for AI acceleration.

Opportunity:

Autonomous systems & smart infrastructure expansion

The expansion of autonomous systems and smart infrastructure presents major opportunities for AI inference chips. Self-driving cars, drones, and robotics rely on real-time inference for navigation, safety, and decision-making. Similarly, smart cities and connected infrastructure demand chips capable of processing massive sensor data streams efficiently. As governments and enterprises invest in automation and digital transformation, inference chips are positioned to capture significant growth, enabling intelligent, adaptive systems across transportation, energy, and urban environments.

Threat:

General-purpose processors improving AI performance

Advances in general-purpose processors, including CPUs and GPUs, pose a threat to specialized inference chips. As mainstream processors integrate AI acceleration features, they reduce the need for dedicated inference hardware in certain applications. This convergence challenges the differentiation of inference chips, particularly in cost-sensitive markets. If general-purpose processors continue to improve AI performance at scale, they may erode demand for niche inference solutions, pressuring specialized vendors to innovate faster to maintain relevance.

Covid-19 Impact:

The COVID-19 pandemic disrupted semiconductor supply chains, delaying production and increasing costs for AI inference chips. However, it also accelerated digital adoption, boosting demand for AI-powered healthcare, remote monitoring, and automation solutions. Inference chips gained traction in medical imaging, diagnostics, and smart devices during the crisis. Post-pandemic recovery reinforced investments in resilient supply chains and localized manufacturing. Ultimately, the pandemic highlighted the importance of inference chips in enabling adaptive, data-driven solutions across critical industries.

The GPUs segment is expected to be the largest during the forecast period

The GPUs segment is expected to account for the largest market share during the forecast period, owing to their versatility and parallel processing capabilities. GPUs accelerate deep learning models, making them indispensable for both training and inference tasks. Their scalability across cloud, edge, and enterprise environments ensures broad adoption. As AI applications expand across industries, GPUs remain the backbone of inference computing, securing the largest market share during the forecast period and reinforcing their role as the primary driver of AI workloads.

The cloud-based segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate, reinforced by the growing adoption of AI-as-a-service platforms. Enterprises increasingly rely on cloud infrastructure to deploy scalable inference workloads without investing in costly on-premises hardware. Cloud providers are integrating specialized inference chips to deliver faster, more efficient AI services. As demand for flexible, cost-effective AI solutions rises, cloud-based inference is expected to lead growth, making it the fastest-expanding segment in the AI inference chips market.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, ascribed to its strong semiconductor manufacturing base and rapid AI adoption in China, Japan, South Korea, and Taiwan. The region benefits from robust investments in AI-driven industries such as consumer electronics, automotive, and smart infrastructure. Government-backed initiatives and expanding R&D centers further strengthen Asia Pacific's leadership. With growing demand for edge AI and cloud services, the region is positioned as the dominant hub for inference chips.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with strong demand from AI, cloud computing, and defense sectors. The presence of leading technology companies and semiconductor innovators drives rapid adoption of inference chips. Government funding for AI research and domestic chip manufacturing initiatives further accelerates growth. As enterprises scale AI deployments across healthcare, finance, and autonomous systems, North America is expected to emerge as the fastest-growing region in the AI inference chips market.

Key players in the market

Some of the key players in AI Inference Chips Market include Advanced Micro Devices (AMD), Intel Corporation, NVIDIA Corporation, Taiwan Semiconductor Manufacturing Company, Samsung Electronics, Marvell Technology Group, Broadcom Inc., Qualcomm Incorporated, Apple Inc., IBM Corporation, MediaTek Inc., Arm Holdings, ASE Technology Holding, Amkor Technology, Cadence Design Systems and Synopsys Inc.

Key Developments:

In November 2025, NVIDIA Corporation reported record-breaking sales of its Blackwell GPU systems, with demand "off the charts" for AI inference workloads in data centers, positioning GPUs as the backbone of generative AI deployments.

In October 2025, Intel Corporation expanded its Gaudi AI accelerator line, integrating advanced inference capabilities to compete directly with NVIDIA in cloud and enterprise AI workloads.

In September 2025, AMD (Advanced Micro Devices) introduced new MI325X accelerators optimized for inference efficiency, targeting hyperscale cloud providers and enterprise AI applications.

Chip Types Covered:

  • Application-Specific Integrated Circuits
  • Graphics Processing Units
  • Central Processing Units
  • Neural Processing Units
  • Field-Programmable Gate Arrays
  • Hybrid AI Chips

Deployments Covered:

  • Cloud-Based
  • Edge Devices
  • On-Premise Data Centers
  • Embedded Systems
  • Mobile Platforms
  • Distributed AI Systems

Applications Covered:

  • Computer Vision
  • Natural Language Processing
  • Speech Recognition
  • Autonomous Systems
  • Recommendation Engines
  • Predictive Analytics

End Users Covered:

  • Technology Companies
  • Automotive OEMs
  • Healthcare Providers
  • Manufacturing Enterprises
  • Retail & E-Commerce
  • Government & Defense

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI Inference Chips Market, By Chip Type

  • 5.1 Introduction
  • 5.2 Application-Specific Integrated Circuits
  • 5.3 Graphics Processing Units
  • 5.4 Central Processing Units
  • 5.5 Neural Processing Units
  • 5.6 Field-Programmable Gate Arrays
  • 5.7 Hybrid AI Chips

6 Global AI Inference Chips Market, By Deployment

  • 6.1 Introduction
  • 6.2 Cloud-Based
  • 6.3 Edge Devices
  • 6.4 On-Premise Data Centers
  • 6.5 Embedded Systems
  • 6.6 Mobile Platforms
  • 6.7 Distributed AI Systems

7 Global AI Inference Chips Market, By Application

  • 7.1 Introduction
  • 7.2 Computer Vision
  • 7.3 Natural Language Processing
  • 7.4 Speech Recognition
  • 7.5 Autonomous Systems
  • 7.6 Recommendation Engines
  • 7.7 Predictive Analytics

8 Global AI Inference Chips Market, By End User

  • 8.1 Introduction
  • 8.2 Technology Companies
  • 8.3 Automotive OEMs
  • 8.4 Healthcare Providers
  • 8.5 Manufacturing Enterprises
  • 8.6 Retail & E-Commerce
  • 8.7 Government & Defense

9 Global AI Inference Chips Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 NVIDIA Corporation
  • 11.2 Intel Corporation
  • 11.3 Advanced Micro Devices
  • 11.4 Qualcomm Incorporated
  • 11.5 Google LLC
  • 11.6 Amazon Web Services
  • 11.7 Microsoft Corporation
  • 11.8 Apple Inc.
  • 11.9 Huawei Technologies
  • 11.10 MediaTek Inc.
  • 11.11 Graphcore Ltd.
  • 11.12 Cerebras Systems
  • 11.13 Groq Inc.
  • 11.14 Mythic AI
  • 11.15 Hailo Technologies
  • 11.16 Ambarella Inc.

List of Tables

  • Table 1 Global AI Inference Chips Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Inference Chips Market Outlook, By Chip Type (2024-2032) ($MN)
  • Table 3 Global AI Inference Chips Market Outlook, By Application-Specific Integrated Circuits (2024-2032) ($MN)
  • Table 4 Global AI Inference Chips Market Outlook, By Graphics Processing Units (2024-2032) ($MN)
  • Table 5 Global AI Inference Chips Market Outlook, By Central Processing Units (2024-2032) ($MN)
  • Table 6 Global AI Inference Chips Market Outlook, By Neural Processing Units (2024-2032) ($MN)
  • Table 7 Global AI Inference Chips Market Outlook, By Field-Programmable Gate Arrays (2024-2032) ($MN)
  • Table 8 Global AI Inference Chips Market Outlook, By Hybrid AI Chips (2024-2032) ($MN)
  • Table 9 Global AI Inference Chips Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 10 Global AI Inference Chips Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 11 Global AI Inference Chips Market Outlook, By Edge Devices (2024-2032) ($MN)
  • Table 12 Global AI Inference Chips Market Outlook, By On-Premise Data Centers (2024-2032) ($MN)
  • Table 13 Global AI Inference Chips Market Outlook, By Embedded Systems (2024-2032) ($MN)
  • Table 14 Global AI Inference Chips Market Outlook, By Mobile Platforms (2024-2032) ($MN)
  • Table 15 Global AI Inference Chips Market Outlook, By Distributed AI Systems (2024-2032) ($MN)
  • Table 16 Global AI Inference Chips Market Outlook, By Application (2024-2032) ($MN)
  • Table 17 Global AI Inference Chips Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 18 Global AI Inference Chips Market Outlook, By Natural Language Processing (2024-2032) ($MN)
  • Table 19 Global AI Inference Chips Market Outlook, By Speech Recognition (2024-2032) ($MN)
  • Table 20 Global AI Inference Chips Market Outlook, By Autonomous Systems (2024-2032) ($MN)
  • Table 21 Global AI Inference Chips Market Outlook, By Recommendation Engines (2024-2032) ($MN)
  • Table 22 Global AI Inference Chips Market Outlook, By Predictive Analytics (2024-2032) ($MN)
  • Table 23 Global AI Inference Chips Market Outlook, By End User (2024-2032) ($MN)
  • Table 24 Global AI Inference Chips Market Outlook, By Technology Companies (2024-2032) ($MN)
  • Table 25 Global AI Inference Chips Market Outlook, By Automotive OEMs (2024-2032) ($MN)
  • Table 26 Global AI Inference Chips Market Outlook, By Healthcare Providers (2024-2032) ($MN)
  • Table 27 Global AI Inference Chips Market Outlook, By Manufacturing Enterprises (2024-2032) ($MN)
  • Table 28 Global AI Inference Chips Market Outlook, By Retail & E-Commerce (2024-2032) ($MN)
  • Table 29 Global AI Inference Chips Market Outlook, By Government & Defense (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.