AIチップセット市場シェア分析、業界動向と統計、成長予測 2026-2031年

AIチップセット市場の規模は、2025年の530億6,000万米ドルから2026年には702億5,000万米ドルに拡大し、2031年には2,859億米ドルに達するとMordor Intelligenceでは予測しています（2026年～2031年の年平均成長率：32.41%）。

この成長を牽引する3つの構造的要因は、計算負荷の高い大規模言語モデル（LLM）に対するかつてない需要、ソフトウェア定義型車両（SDV）の採用加速、そして超低消費電力エッジ向けシリコン技術の飛躍的進歩です。性能向上は依然として高度なパッケージング技術や広帯域メモリ（HBM）に大きく依存していますが、3nm未満の微細化プロセスにおける供給制約が、短期的には生産量の拡大を制限しています。その一方で、輸出規制、エネルギー効率に関する義務化、サステナビリティ目標といった要素が調達戦略を変化させており、ワットあたりの性能（電力効率）に優れたアーキテクチャが選好されるようになっています。高い処理能力（スループット）、熱効率、そしてサプライチェーンの強靭さのバランスを最適化できる市場参入企業は、データセンター、自動車、エッジといったあらゆる主要分野において、長期的な採用（デザインウィン）を勝ち取っています。こうした動向を総合すると、AIチップセット市場は2030年に向けて、生成AI、ソブリンクラウド戦略、オンデバイス・インテリジェンスを実現するための基盤的役割を担うことになります。

レポートの主なポイント

• コンポーネント別では、2025年のAIチップセット市場においてGPUが51.40%のシェアを占めて首位となりましたが、NPUやASICも2031年にかけて年平均成長率（CAGR）44.2%で拡大すると予測されています。
• 処理タイプ別では、2025年の市場規模において学習（トレーニング）ワークロードが60.30%のシェアを占めました。一方、推論（インファレンス）分野は2031年にかけてCAGR 36.9%で成長しています。
• 導入場所別では、2025年のAIチップセット市場においてクラウドおよびハイパースケール・データセンターが63.10%のシェアを占めましたが、エッジデバイス市場は2031年にかけてCAGR 39.2%で成長すると予測されています。
• 用途別では、2025年の市場規模において家電製品（コンシューマーエレクトロニクス）が26.40%のシェアを占めました。自動車・輸送分野は、2031年まで42.6%という最も高い年平均成長率（CAGR）を記録すると予測されています。
• 地域別では、2025年時点でアジア太平洋地域がAIチップセット市場シェアの41.10%を占めました。一方、中東・アフリカ地域は、2031年まで34.1%のCAGRで成長すると見込まれています。
• ベンダー別では、NVIDIA、AMD、Intel、Google、Amazonの各社が、2024年時点で学習用アクセラレータ市場シェアの80%以上を占めていました。

コンポーネント別：メモリの統合がシリコンの進化を牽引

NPUやASICが2031年までに年平均成長率（CAGR）44.2%で成長すると予測される中、GPUは学習処理における圧倒的な並列処理能力を武器に、2025年時点でAIチップセット市場の51.40%のシェアを維持しました。フロンティアモデルの巨大化に伴い計算リソースへの投資額が増大しているため、GPU出荷に伴う市場規模は絶対額ベースでは今後も拡大し続けますが、特定用途向けシリコン（ドメインスペシフィック・シリコン）へとシェアが移行していることは明白です。メモリおよびストレージのサプライヤーは、極めて大きな追い風を受けています。例えば、SamsungのHBM3Eスタックは現在1ダイあたり36GBに達しており、より大きなコンテキストウィンドウへの需要に応えつつ、平均販売価格（ASP）の上昇にも寄与しています。2024年以降、HBMの価格が500%上昇したという事実は、市場が単なる動作周波数よりも帯域幅を重視していることを裏付けています。チップレット技術に基づくヘテロジニアス（異種混載）な設計では、CPU、NPU、HBMを共通のインターポーザー上に統合し、エッジでの推論処理における電力効率（パワーエンベロープ）の最適化を図っています。高度な2.5Dパッケージング、ダイ間インターコネクト、メモリの近接配置（コ・ロケーション）技術を習得したベンダーは、進化を続けるAIチップセット市場において高い利益率を確保することになるでしょう。

CPUセグメントは、オンダイAIアクセラレータや新しい命令セットの導入によって適応を図り、制御ロジックと推論処理が混在する従来のワークロードにおいてその重要性を維持しています。FPGAは、絶対的なスループットよりも決定論的なレイテンシ（応答時間の確実性）や現場でのアップグレード性が重視される領域、特に産業用ロボットや通信用ゲートウェイにおいて再び勢いを増しています。アーキテクチャの多様性は、最終的に市場全体の規模（TAM）を拡大させることにつながります。なぜなら、各ワークロードに対して最適なシリコンブロックを割り当てることが可能になるからです。したがって、システムインテグレーターが個別の部品ではなくターンキー方式のサブシステムを求めるようになる中、複数のチップレットを組み合わせたソリューションを統合・提供できるサプライヤーは、大幅なシェア拡大を実現する有利な立場にあります。

AI Chipsets Market Analysis by Mordor Intelligence

AI Chipsets Market size in 2026 is estimated at USD 70.25 billion, growing from 2025 value of USD 53.06 billion with 2031 projections showing USD 285.9 billion, growing at 32.41% CAGR over 2026-2031.

Unprecedented demand for compute-intensive large language models, accelerating adoption of software-defined vehicles, and breakthroughs in ultra-low-power edge silicon are the three structural forces propelling this growth. Performance gains remain strongly tied to advanced packaging and high-bandwidth memory, yet supply constraints below the 3 nm node limit near-term output. Meanwhile, export-control ceilings, energy-efficiency mandates, and sustainability targets are reshaping sourcing decisions and favoring architectures that can deliver higher performance per watt. Market participants able to balance raw throughput with thermal efficiency and supply-chain resilience are securing long-term design wins in data-center, automotive, and edge deployments across every major region. Collectively, these dynamics position the AI chipsets market as a foundational enabler of generative AI, sovereign-cloud strategies, and on-device intelligence through 2030.

Key Report Takeaways

• By component, GPUs led with 51.40% of AI chipsets market share in 2025, while NPUs and ASICs are set to expand at a 44.2% CAGR through 2031.
• By processing type, training workloads commanded 60.30% share of the market size in 2025; inference is advancing at a 36.9% CAGR to 2031.
• By deployment location, cloud and hyperscale data centers held 63.10% share of the AI chipsets market size in 2025, whereas edge devices are projected to grow at a 39.2% CAGR through 2031.
• By application, consumer electronics captured 26.40% share of the market size in 2025; automotive and transportation is forecast to post the fastest 42.6% CAGR to 2031.
• By geography, Asia-Pacific accounted for 41.10% of AI chipsets market share in 2025, while the Middle East and Africa region is expected to increase at a 34.1% CAGR through 2031.
• On the vendor front, NVIDIA, AMD, Intel, Google, and Amazon collectively controlled more than 80% of the training-accelerator market share in 2024.

Note: Market size and forecast figures in this report are generated using Mordor Intelligence’s proprietary estimation framework, updated with the latest available data and insights as of 2026.

Global AI Chipsets Market Trends and Insights

Drivers Impact Analysis*

Exploding Training-Compute Demand from Frontier-Model Developers

Annual compute needs for large-scale language models are increasing ten-foldev ery 18 months, driving sustained orders for multi-die GPUs and advanced packaging solutions. NVIDIA’s data-center revenue rose to USD 35.6 billion in Q4 2025 on the back of Blackwell supercomputer shipments, underscoring how hyperscale customers are accumulating vast AI-specific inventories. Multimodal model builders now require thousands of interconnected accelerators, pushing demand for CoWoS substrates and next-generation HBM stacks. As a result, leading AI companies are expected to control 15–20% of global AI-compute capacity by 2027, ensuring continuous procurement of 3 nm-class silicon. This volume concentration intensifies near-term shortages but establishes a multi-year revenue pipeline for suppliers that can execute at advanced nodes. Consequently, the AI chipsets market will benefit from a structurally higher baseline of training-oriented purchases through the forecast horizon.

Automotive “Software-Defined Vehicle” Silicon Design Wins

Automakers are consolidating scores of electronic control units into centralized AI-enabled compute domains. Industry analysts project that 80% of new vehicles will embed AI functionality by 2035, creating a large installed base for inference-class accelerators. NXP’s S32N processors built on 5 nm technology deliver 34 TOPS while meeting rigorous ASIL D requirements, signalling that automotive-grade safety and AI horsepower can coexist in a single device. With each design cycle spanning seven to ten years, silicon selected for today’s models generates annuity-like volume for their suppliers. Design wins now being awarded for Level 3 autonomy, sensor fusion, and over-the-air upgradability will therefore compound demand during the forecast period.

Ultra-Low-Power Edge AI ASIC Breakthroughs

Edge-native processors bring real-time inference to mobile, IoT, and industrial endpoints that cannot depend on cloud connectivity. Syntiant’s NDP250 delivers 30 GOPS within a milliwatt-class envelope, enabling always-on voice assistants and local LLM interactions. BrainChip’s neuromorphic coprocessor similarly harnesses event-driven processing to minimize power draw, unlocking AI in battery-powered sensors and wearables. These breakthroughs allow OEMs to embed intelligence without compromising form factor or battery life, accelerating design wins in healthcare diagnostics, predictive maintenance, and human-machine interfaces. The speed with which volume consumer products integrate such ASICs magnifies unit-shipment trajectories, adding yet another tailwind to the AI chipsets market.

National AI-Infrastructure Stimulus Programs

Sovereign AI agendas are rewriting semiconductor demand curves. The CHIPS and Science Act earmarks more than USD 50 billion for US foundry capacity and R&D, while China has committed USD 143 billion toward AI self-reliance. The UAE’s USD 200 billion AI infrastructure master plan—with NVIDIA chips at its core—propels fresh demand in the Middle East. Such fiscal programs subsidize fabs, data centers, and packaging facilities, anchoring local demand that is less sensitive to global macro cycles. The resulting procurement of AI accelerators, specialty memory, and interconnect silicon boosts addressable volumes for ecosystem players and sustains the market’s double-digit expansion into the medium term.

Restraints Impact Analysis*

Supply-Chain Lithography Bottlenecks Below 3 nm

High-NA EUV machines required for 2 nm production cost more than USD 300 million each and remain limited in quantity. TSMC’s first 2 nm pilot line enters mass production in late 2025 but faces heavy pre-allocations from flagship customers. Capacity scarcity drives higher wafer pricing and lengthens delivery lead times for AI accelerators fabricated on these nodes. China’s exclusion from High-NA lithography further fragments global supply chains, raising the prospect of dual technology standards. The net effect lowers near-term unit availability and tempers the AI chipsets market growth rate until additional fabs come online after 2027.

AI-Model Compression Reducing Silicon Requirements

Pruning, quantization, and knowledge distillation techniques can cut inference compute demand by up to 70%, enabling smaller models to match original accuracy. If broadly adopted, these methods would lessen the need for high-end chips in certain workloads and moderate overall silicon volume growth. Consequently, compression innovation represents a structural counterforce to the market even as it enables wider AI deployment.

*Our forecasts treat driver/restraint impacts as directional, not additive. The impact forecasts reflect baseline growth, mix effects, and variable interactions.

Segment Analysis

By Component: Memory Integration Drives Silicon Evolution

GPUs retained 51.40% AI chipsets market share in 2025 by delivering unmatched parallelism for training, even as NPUs and ASICs are forecast to grow at a 44.2% CAGR by 2031. The market size allocated to GPU shipments will continue rising in absolute terms as frontier models swell compute budgets, yet the share shift toward domain-specific silicon is unmistakable. Memory and storage suppliers enjoy extraordinary tailwinds: HBM3E stacks from Samsung now reach 36 GB per die, meeting larger context window demands while raising average selling prices. A 500% increase in HBM pricing since 2024 confirms the market’s appetite for bandwidth over raw frequency. Heterogeneous designs based on chiplets are integrating CPUs, NPUs, and HBM on a common interposer to optimize power envelopes for edge inference. Vendors that master advanced 2.5D packaging, die-to-die interconnects, and memory co-location will capture premium margins within the evolving AI chipsets market.

The CPU segment adapts through on-die AI accelerators and new instruction sets, preserving relevance in traditional workloads that intermingle control logic and inference. FPGAs regain momentum where deterministic latency or in-field upgradability outweighs absolute throughput, especially inside industrial robots and telecom gateways. Architectural diversity ultimately raises the total addressable market because each workload maps to the most efficient silicon block. Suppliers capable of orchestrating multi-chiplet solutions are thus positioned for outsized share gains as system integrators demand turnkey subsystems rather than discrete parts.

By Processing Type: Inference Acceleration Reshapes Silicon Priorities

Training commanded 60.30% of AI chipsets market share in 2025, anchored by hyperscale data-center clusters running hundreds of petaflops per rack. The market size linked to training will keep growing because parameter counts in multimodal models expand geometrically; scenarios point to 100 million H100-class GPUs required by 2030. Still, inference shipments will scale at a 36.9% CAGR as enterprises roll out generative AI services across verticals and embed smaller models at the network edge. Cerebras Systems and Qualcomm jointly demonstrated 10× price-performance gains versus incumbent solutions, confirming that fresh architectures can disrupt historical cost curves.

Edge inference accelerators place energy efficiency above FLOPS, spurring chip vendors to adopt low-voltage SRAM, near-memory compute, and analog processing for kernels such as attention or convolution. This dichotomy creates two parallel product roadmaps: ultra-dense, liquid-cooled dies for training, and svelte, milliwatt-class ASICs for inference. Vendors that straddle both categories can cross-sell software toolchains, while specialists may seize niches around latency, security, or price-sensitive endpoints. The resulting competitive tension sustains innovation across the AI chipsets market.

By Deployment Location: Edge Computing Drives Architectural Innovation

Cloud facilities generated 63.10% of AI chipsets market size in 2025 as hyperscalers funnelled USD 500 billion into new data-center builds. Despite this dominance, edge deployments are forecast to increase at a 39.2% CAGR, reflecting demand for real-time analytics, reduced backhaul cost, and data-sovereignty compliance. Enterprises are also repatriating select AI workloads to on-premises clusters equipped with Intel’s Gaudi 3 accelerators, which provide 50% higher inference throughput than NVIDIA’s H100 at lower cost. These trends create a mosaic of deployment models, public cloud, private cloud, hybrid, and far-edge, that collectively diversify revenue streams for silicon suppliers.

On-device inference in vehicles, drones, and industrial controllers favours chiplets that pack domain-specific cores next to general-purpose logic. Thermal envelopes and ruggedization needs require innovations such as silicon carbide substrates, phase-change materials, and direct-inlet liquid coolers. Consequently, the market will fragment into form-factor sub-segments, each optimized for analogous environment and power constraints yet unified by common software runtimes.

By Application: Automotive Transformation Accelerates Silicon Demand

Consumer electronics accounted for 26.40% of the AI chipsets market size in 2025, a figure inflated by smartphone and PC refresh cycles integrating on-device generative AI. However, automotive and transportation are projected to grow at a 42.6% CAGR through 2031, overtaking consumer electronics by the decade’s close. Qualcomm estimates that software-defined vehicles could unlock a USD 650 billion annual semiconductor opportunity by 2030. Renesas’ R-Car V4H SoC delivers 34 TOPS at 16 TOPS/W, proving that high-grade functional safety and AI performance converge inside a single die, thus meeting OEM cost envelopes while future-proofing compute headroom.

Healthcare and life sciences apply high-throughput AI chipsets in imaging and genomics, whereas industrial and robotics segments demand deterministic latency and extended operating ranges. The enterprise IT and BFSI domain integrates accelerators into servers for fraud analytics and conversational agents. Each vertical’s distinct latency, power, and standards profile pushes vendors to customize silicon blocks, interactive compilers, and security modules. This heterogeneity ensures multipronged demand streams underpin the AI chipsets market.

Complete Report Scope:

• By Component
  • Central Processing Unit (CPU)
  • Graphics Processing Unit (GPU)
  • Neural Network Processor (NNP)
  • Other Components
• By Application
  • Consumer Electronics
  • Automotive
  • Healthcare
  • Automation and Robotics
  • Other Applications
• By Geography
  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle-East

Geography Analysis

Asia-Pacific maintained leadership with 41.10% AI chipsets market share in 2025. China’s USD 143 billion AI-self-reliance program, Taiwan’s >90% share in advanced AI manufacturing, and South Korea’s hegemony in HBM reinforce the region’s advantage. Japan’s Fugaku supercomputer upgrade further cements local demand for training-class accelerators. As a result, the market size tied to Asia-Pacific will expand steadily despite near-term export-control friction.

North America benefits from a deep R&D ecosystem, hyperscale capex, and government subsidies under the CHIPS and Science Act. NVIDIA’s platform dominance and Intel’s foundry reshore strategy tighten regional supply-chain control while preserving access to bleeding-edge capacity. These factors keep North America as the second-largest consumption base, especially for training clusters and custom accelerators for cloud providers.

The Middle East and Africa region, although smaller in absolute terms, is projected to post a 34.1% CAGR, making it the fastest-growing territory in the AI chipsets market. The UAE’s Stargate campus anchored by NVIDIA GPUs and Saudi Arabia’s Vision 2030 USD 40 billion AI fund draw direct investment from Western tech firms. Customizations for desert-climate thermals and Arabic-language LLMs broaden the application spectrum, underscoring how local conditions can trigger tailored silicon solutions. Europe remains focused on data sovereignty and energy efficiency, championing GAIA-X cloud standards that influence spec selection toward lower-power AI chipsets. South America is an emerging adopter, leveraging edge AI for agriculture and natural-resource monitoring, yet still trails on advanced-node access.

Value Chain Analysis
The AI chipsets value chain covers EDA/IP and architecture design (chip vendors and hyperscalers), wafer fabrication at advanced nodes, and advanced packaging and test. It also includes HBM and other memory supply, followed by system integration and distribution into cloud data centers, automotive platforms, and edge devices. In 2025-2026, the binding constraints have shifted from wafer availability alone to a combined gate of advanced packaging throughput (notably CoWoS-class 2.5D/3D integration) and HBM stacking capacity. This elevates the strategic role of memory suppliers such as SK hynix, Samsung, and Micron alongside leading foundry packaging operations. As a result, co-design across silicon, interconnect, packaging, and memory has become more central to meeting performance-per-watt and time-to-deployment targets.

Downstream, OEMs and AI infrastructure integrators are pulling the supply chain closer through multiyear technology and manufacturing partnerships, reflecting the need to lock in packaging and memory roadmaps early in the design cycle. For example, NVIDIA and SK hynix announced a multiyear partnership in June 2026 to advance next-generation memory for AI factories. Separately, Flex and Cerebras expanded manufacturing in California in July 2026 to scale CS-3 system production, while onshoring and multi-partner manufacturing collaborations have gained visibility. NVIDIA’s highlighted U.S. manufacturing activity with TSMC in Phoenix, along with collaborations with partners such as Foxconn and Wistron, points to tighter coupling between chip designers, foundry/OSAT ecosystems, and system builders to reduce lead-time risk and improve supply resilience.

Competitive Landscape

The AI chipsets market is characterized by high concentration. NVIDIA alone controls roughly 80% of training-accelerator revenue, buoyed by the comprehensive CUDA software stack and Blackwell-generation hardware that posted USD 11 billion in quarterly sales. AMD’s MI300 series has narrowed the gap, surpassing USD 1 billion in quarterly revenue and improving supply diversity. Hyperscalers now design custom silicon such as Google’s TPU v5e and Amazon’s Graviton4 to lower total cost of ownership and reduce vendor dependence, signifying a gradual vertical-integration shift.

Intel stakes its comeback on the Gaudi 3 accelerator, which claims 50% higher inference throughput than peer GPUs while positioning its foundry as an open manufacturing alternative. Start-ups like Cerebras Systems, Groq, and SiMa.ai are disrupting niches with wafer-scale engines, token-optimized pipelines, and multimodal edge ASICs. Meanwhile, memory vendors Samsung and SK Hynix expand HBM capacity through multibillion-dollar fabs, recognizing that memory bandwidth has become the new performance bottleneck. As the market approaches USD 226 billion by 2030, competitive dynamics will intensify around software ecosystems, heterogeneous integration, and total-system optimization rather than raw die size alone.

Recent Industry Developments

• June 2026: NVIDIA and SK hynix announced a multiyear technology partnership to co-develop next-generation memory for AI factories, targeting integration with NVIDIA platforms including Vera Rubin systems and Jetson Thor. The partnership tightens the coupling between accelerator roadmaps and HBM innovation, where bandwidth and packaging compatibility increasingly shape delivered performance and shipment timing.
• September 2025: NVIDIA and Intel announced a collaboration to develop AI infrastructure and personal computing products, including work to integrate NVIDIA NVLink with Intel x86 platforms. The agreement indicates deeper cross-vendor system-level engineering to broaden procurement options for data centers and accelerate deployment-ready reference designs across enterprise and cloud ecosystems.
• September 2024: Intel unveiled next-generation AI solutions with the launch of Xeon 6 and Gaudi 3. The product push reinforced Intel’s position in AI servers by pairing general-purpose CPUs with dedicated accelerators, giving hyperscalers and enterprises an alternate stack for training and inference deployments.

List of Companies Covered in this Report:

• Advanced Micro Devices Inc. (AMD)
• Xilinx Inc.
• Graphcore Ltd
• Huawei Technologies Co. Ltd
• IBM Corporation
• Intel Corporation
• NVIDIA Corporation
• Micron Technology Inc.
• Samsung Semiconductor (Samsung Electronics Co. Ltd)

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 INSIGHTS
4.1 Market Overview
4.2 Industry Attractiveness – Porter’s Five Forces Analysis
4.2.1 Threat of New Entrants
4.2.2 Bargaining Power of Buyers
4.2.3 Bargaining Power of Suppliers
4.2.4 Threat of Substitute Products
4.2.5 Intensity of Competitive Rivalry
4.3 Industry Value Chain Analysis
4.4 Assessment of the Impact of COVID-19 on the AI Chipsets Market

5 MARKET DYNAMICS
5.1 Market Drivers
5.1.1 Increase in Demand for Autonomous Driving Technology
5.1.2 Growth in Edge Analytics for IoT Application
5.2 Market Restraints
5.2.1 Complexity in Design and AI Interface

6 MARKET SEGMENTATION
6.1 By Component
6.1.1 Central Processing Unit (CPU)
6.1.2 Graphics Processing Unit (GPU)
6.1.3 Neural Network Processor (NNP)
6.1.4 Other Components
6.2 By Application
6.2.1 Consumer Electronics
6.2.2 Automotive
6.2.3 Healthcare
6.2.4 Automation and Robotics
6.2.5 Other Applications
6.3 By Geography
6.3.1 North America
6.3.2 Europe
6.3.3 Asia-Pacific
6.3.4 Latin America
6.3.5 Middle-East

7 COMPETITIVE LANDSCAPE
7.1 Company Profiles
7.1.1 Advanced Micro Devices Inc. (AMD)
7.1.2 Xilinx Inc.
7.1.3 Graphcore Ltd
7.1.4 Huawei Technologies Co. Ltd
7.1.5 IBM Corporation
7.1.6 Intel Corporation
7.1.7 NVIDIA Corporation
7.1.8 Micron Technology Inc.
7.1.9 Samsung Semiconductor (Samsung Electronics Co. Ltd)

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET

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