Dublin, Nov. 05, 2026 (GLOBE NEWSWIRE) -- "Accelerated Computing - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026-2031)" has been added to ResearchAndMarkets.com's offering.
Accelerated Computing Market to Reach USD 546.42 Billion by 2031
The accelerated computing market is projected to expand from USD 180.72 billion in 2025 to USD 217.82 billion in 2026, reaching USD 546.42 billion by 2031. The market is forecast to register a 20.19% CAGR during 2026-2031, supported by rising artificial intelligence workloads, increasing large language model complexity, edge inference adoption, and sustained investment in high-performance data center infrastructure.
Large Language Model Training Drives Infrastructure Investment
Frontier model training has become a major focus of capital allocation across the accelerated computing industry. Epoch AI data indicated that training compute for frontier language models increased fivefold annually from 2020, while associated power requirements doubled each year. By mid-2026, the largest known training run had consumed 510? FLOPs, representing 24 times the compute used for GPT-4.
Hardware requirements are also extending beyond pre-training. Fine-tuning, pruning, reinforcement learning, and other post-training processes lengthen the compute cycle and increase demand for advanced processors, networking, memory, cooling, and rack-level systems. In June 2026, NVIDIA's Blackwell platform demonstrated this shift through MLPerf Training v6.0 results, with DeepSeek-V3 671B trained in 2.02 minutes using 8,192 GB300 NVL72 GPUs at CoreWeave. These developments favor suppliers capable of delivering integrated rack-scale performance rather than relying exclusively on individual chip improvements.
Edge Inference Creates New Accelerated Computing Opportunities
Inference workloads are moving into devices and operational systems that cannot depend on remote cloud infrastructure for every task. Automotive platforms, industrial equipment, connected medical systems, and robotics increasingly require low-latency processing, local responsiveness, and improved energy efficiency. This trend is strengthening demand for ASICs, FPGAs, NPUs, and specialized inference engines.
Agentic AI is further influencing processor selection by separating prefill and decode stages, allowing different hardware architectures to manage specific parts of a workflow. In December 2025, NVIDIA entered a USD 20 billion licensing agreement with Groq to incorporate LPU dataflow engines into liquid-cooled Rubin-generation LPX racks for low-latency inference. AWS also made EC2 G7 instances generally available in June 2026 with NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, providing up to 4.6x higher AI inference performance than the previous generation.
Advanced Manufacturing Capacity Remains a Constraint
Sub-5 Nm manufacturing capacity continues to present a supply-side challenge. In June 2026, demand for advanced nodes was reported to exceed available capacity by 25-30%, with meaningful relief not expected before 2027. Advanced packaging used to connect accelerator dies with high-bandwidth memory also remains constrained.
These limitations can delay shipments even after processor designs are completed, particularly for smaller accelerator developers without secured foundry and packaging allocations. Export-control uncertainty adds another layer of complexity, while hyperscaler custom silicon programs and energy-efficient accelerator architectures continue to reshape procurement strategies.
GPU Leadership Continues as Custom ASIC Adoption Accelerates
GPUs represented 55.34% of the accelerated computing market in 2025. Their leadership reflects strong AI framework integration, mature software ecosystems, developer familiarity, and broad availability through enterprise and cloud platforms. GPUs remain the preferred option for organizations requiring immediate access to high-throughput AI training and flexible inference capabilities.
Custom ASICs are forecast to record a 21.32% CAGR through 2031, making them the fastest-growing processor category. Adoption is being led by hyperscalers with large, predictable workloads and greater control over internal software. Google's TPU roadmap and Microsoft's Maia 200 deployment illustrate the growing focus on performance per watt and per dollar. FPGAs retain an important position in reconfigurable and low-latency applications, while CPUs and NPUs are gaining traction in cost-sensitive edge inference environments.
Data Centers Lead Revenue While Edge Deployment Grows Faster
On-premises and data center deployments accounted for 51.48% of market revenue in 2025. Hyperscalers, colocation operators, and large enterprises continue to invest in clustered systems for model training, large-batch inference, and secure internal AI workloads. Power availability, cooling capacity, rack density, and orchestration software remain central purchasing considerations.
Edge and embedded deployment is expected to grow at a 21.51% CAGR through 2031. Autonomous vehicles, industrial robots, and connected healthcare systems require deterministic local processing without round-trip cloud reliance. Cloud infrastructure will remain important for organizations unable to fund private clusters, while high-utilization users may favor ownership models offering stronger long-term economics.
North America Leads as Asia-Pacific Records the Fastest Growth
North America held 41.26% of the accelerated computing market in 2025, supported by concentrated hyperscaler investment, mature AI software ecosystems, and a substantial enterprise customer base. The United States leads regional data center development, while Canada is adding capacity in Ontario and Quebec. Mexico is also benefiting from nearshore supply-chain investment. Export compliance remains significant following the January 2026 BIS framework affecting overseas sales and customer certification requirements.
Asia-Pacific is projected to achieve the fastest regional CAGR at 21.65% through 2031. Growth is supported by sovereign AI programs and funded semiconductor infrastructure. Japan committed USD 13 billion through METI to semiconductor and industrial AI initiatives, while Microsoft announced plans to invest JPY 1.6 trillion, equivalent to USD 10.3 billion, in Japan between 2026 and 2029. South Korea approved USD 5.7 billion for national AI infrastructure in May 2026, including a national compute center equipped with 15,000 GPUs. Europe is also generating demand through automotive computing, industrial automation, financial services, and sovereign data initiatives.
Key Topics Covered:
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 Market Drivers
4.2.1 Rising Large Language Model Training Intensity
4.2.2 Rapid Expansion of Edge Inference Workloads
4.2.3 Hyperscaler Custom Silicon Adoption
4.2.4 Growing Adoption of Energy-Efficient Accelerator Architectures
4.2.5 Advanced Cooling and Rack Density Optimization
4.2.6 Silicon Photonics and Optical Interconnect Integration
4.3 Market Restraints
4.3.1 Sub-5 Nm Capacity Concentration
4.3.2 High Total Cost of Ownership for Liquid-Cooled Clusters
4.3.3 Export-Control Uncertainty for Advanced Accelerators
4.3.4 HBM and Advanced Packaging Bottlenecks
4.4 Industry Value Chain Analysis
4.5 Regulatory Landscape
4.6 Technological Outlook
4.7 Impact of Macroeconomic Factors on the Market
4.8 Porter's Five Forces Analysis
4.8.1 Threat of New Entrants
4.8.2 Bargaining Power of Suppliers
4.8.3 Bargaining Power of Buyers
4.8.4 Threat of Substitutes
4.8.5 Competitive Rivalry
5 MARKET SIZE AND GROWTH FORECASTS (VALUE)
5.1 By Processor Type
5.1.1 Graphics Processing Unit (GPU)
5.1.2 Application-Specific Integrated Circuit (ASIC)
5.1.3 Field-Programmable Gate Array (FPGA)
5.1.4 Central Processing Unit (CPU) and Neural Processing Unit (NPU)
5.2 By Deployment
5.2.1 On-Premises and Data Center
5.2.2 Cloud-Based
5.2.3 Edge and Embedded
5.3 By Function
5.3.1 Training
5.3.2 Inference
5.4 By End User
5.4.1 Hyperscale Cloud Service Providers
5.4.2 Enterprise and Colocation Data Centers
5.4.3 Automotive OEMs and Tier-1 Suppliers
5.4.4 Healthcare and Life Sciences
5.4.5 Financial Services
5.4.6 Telecom and 5G Infrastructure
5.4.7 Government, Defense, and Research
5.4.8 Manufacturing and Industrial Automation
5.5 By 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 Vendor Positioning Analysis
6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
6.4.1 NVIDIA Corporation
6.4.2 Advanced Micro Devices, Inc.
6.4.3 Intel Corporation
6.4.4 Qualcomm Incorporated
6.4.5 Broadcom Inc.
6.4.6 Marvell Technology, Inc.
6.4.7 Cerebras Systems, Inc.
6.4.8 Groq, Inc.
6.4.9 Tenstorrent Inc.
6.4.10 Graphcore Limited
6.4.11 SambaNova Systems, Inc.
6.4.12 Rebellions Inc.
6.4.13 Hailo Technologies Ltd.
7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK
7.1 White-Space and Unmet-Need Assessment
For more information about this report visit https://www.researchandmarkets.com/r/bhwbdg
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