AI 프레임워크 최적화 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI framework optimization market size is expected to grow from USD 4.51 billion in 2025 to USD 5.83 billion in 2026 and is forecast to reach USD 18.66 billion by 2031 at 26.20% CAGR over 2026-2031.

This report is Segmented by Solution Type (Model Optimization and Compression Software, and More), Deployment Environment (On-Premises and Private Cloud, On-Device AI, and More), Organization Size (Large Enterprises, and Small and Medium Enterprises), Application (Robotics, Autonomous Systems, and Edge Intelligence, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global AI Framework Optimization Market Trends and Insights

Rising Demand for Ultra-Low Latency Inferencing

Latency is now a basic operating requirement in conversational AI, fraud detection, industrial control, robotics, and other live production systems. The AI framework optimization market is benefiting because every improvement in response time now has a direct effect on user experience, infrastructure utilization, and service consistency. This pressure is stronger in agentic systems, where a single workflow can trigger multiple model calls, retrieval steps, and tool actions before a result is returned. NVIDIA reported in February 2026 that DFlash speculative decoding on Blackwell architecture delivered throughput gains of up to 15x on specific workloads, which shows that large performance headroom still exists at the software layer. That remaining headroom keeps buyers focused on batching, caching, token scheduling, and speculative execution rather than treating inference speed as a solved problem. The AI framework optimization market therefore continues to draw spending into serving software and runtime controls that can hold latency inside production thresholds as workloads become more complex.

Growth of Generative AI And Agentic Workflows

Generative AI has moved beyond isolated pilots and now sits closer to real business processes, customer support flows, developer tools, and internal knowledge systems. The AI framework optimization market is gaining from this shift because agentic workflows multiply inference events faster than traditional single-step AI use cases. Each added reasoning pass, retrieval loop, and external tool call increases memory pressure, token throughput requirements, and the need for better execution planning. NVIDIA introduced Vera in March 2026 as a processor purpose-built for agentic AI, which signals that vendors are already redesigning systems around the heavy runtime behavior of multi-step AI workloads. The practical result is that enterprises are placing more value on orchestration layers that can manage prompts, context, model routing, and repeated execution without unacceptable delay. As agentic designs spread, the AI framework optimization market is likely to stay closely linked to serving efficiency rather than only to model innovation.

High Cost of Specialized AI Infrastructure

Optimization still depends on direct access to the hardware on which models will actually run in production. The AI framework optimization market therefore remains constrained by the cost of accelerator fleets, high-performance servers, and the supporting power and cooling capacity needed to test at scale. This burden is heavier for mid-market buyers and for regions where compute availability and data center readiness are less developed. Cloud access helps, but it can also add recurring expense and reduce direct control over benchmarking, kernel tuning, and validation cycles. The European Commission proposed the Cloud and AI Development Act in June 2026 to create an EU-wide framework for trusted cloud and AI development, which could improve access over time, but this is still a policy response rather than an immediate infrastructure fix. Until access broadens materially, the AI framework optimization market will continue to face slower adoption among organizations that want efficiency gains but cannot secure enough specialized compute to optimize effectively.

Other drivers and restraints analyzed in the detailed report include:

1. Rising Enterprise Spending on AI Runtime Efficiency
2. Expansion of Edge AI and On-Device Intelligence
3. Framework Fragmentation and Integration Complexity

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

AI Inference Serving and Orchestration Software held 27.11% of the AI framework optimization market share in 2025, which made it the largest solution segment. Its lead reflects the fact that optimization only creates visible business value when models can be served reliably in production with stable latency and availability. Enterprises often begin with serving and orchestration because this layer connects infrastructure decisions directly to user experience, service continuity, and operating cost. The segment also benefits from growing use of agentic workflows, where repeated model calls require stronger routing, caching, and session control than earlier AI deployments. In practical terms, this keeps the AI framework optimization market centered on software that can operationalize models at scale rather than simply improve isolated benchmark scores.

The AI framework optimization market size for Model Optimization and Compression Software is projected to expand at 27.21% CAGR through 2031, making it the fastest-growing solution segment. This growth reflects the commercial push to extract more throughput from existing compute rather than solve every deployment problem with new hardware purchases. ACL Anthology research published in 2025 showed that careful W8A8-INT quantization narrowed the reported accuracy gap versus FP8 to 0.7 points on large models, which helped validate production-grade compression pathways for larger deployments. Graph compilation, runtime acceleration, profiling, observability, and managed services remain important because each handles a different stage between model preparation and live execution. Taken together, these layers give the AI framework optimization market a broad solution mix where no single category can replace the others across all customer environments.

Cloud and Hyperscale Data Centers accounted for 54.33% of the AI framework optimization market size in 2025, which kept cloud infrastructure as the main revenue base for deployment. This position reflects the scale at which hyperscalers and large enterprises run shared inference platforms, centralized model updates, and heavy production workloads. Cloud environments also make it easier to roll out optimization changes once and distribute the benefit across many users, teams, and services. For organizations moving from pilot work into sustained production, that operational simplicity remains a strong advantage. As a result, the AI framework optimization market continues to send a large share of spending toward cloud-native serving, scheduling, and observability tools.

The AI framework optimization market size for On-Device AI is projected to expand at 27.62% CAGR through 2031, the fastest rate among deployment environments. Local execution is gaining because privacy requirements, weak connectivity, and strict response-time targets make many workloads difficult to support through cloud-only inference. NVIDIA introduced TensorRT Edge-LLM in 2026 for embedded automotive and robotics inference, which highlights the rise of device-specific optimization stacks. On-premises, edge infrastructure, and hybrid models are also becoming more relevant because many organizations now split workloads across public and private environments instead of relying on a single runtime. This diversification means the AI framework optimization market increasingly rewards vendors that can manage portability, governance, and performance across several deployment paths at once.

Complete Report Scope:

• By Solution Type
  • Model Optimization and Compression Software
  • Graph Compilation and Kernel Optimization Software
  • AI Runtime and Hardware Acceleration Software
  • AI Inference Serving and Orchestration Software
  • Performance Profiling, Benchmarking, and Observability Tools
  • Professional and Managed Optimization Services
• By Deployment Environment
  • Cloud and Hyperscale Data Centers
  • On-Premises and Private Cloud
  • Edge Infrastructure
  • On-Device AI
  • Hybrid Deployment
• By Organization Size
  • Large Enterprises
  • Small and Medium Enterprises
• By Application
  • Generative AI, Large Language Models, and Multimodal AI
  • Natural Language Processing and Document Intelligence
  • Computer Vision and Video Analytics
  • Speech and Audio AI
  • Recommendation, Search, and Personalization Engines
  • Predictive Analytics, Classical ML, and Decision Intelligence
  • Robotics, Autonomous Systems, and Edge Intelligence
  • Other Applications
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America accounted for 48.44% of the AI framework optimization market size in 2025, which kept the region in the lead on revenue. The United States anchors this position through hyperscale cloud capacity, a dense vendor ecosystem, and a steady pace of product launches across inference software and AI hardware. Canada adds regional depth through its research base and commercialization networks, which help move model work into deployable runtime and serving tools. South America remains smaller, but interest is rising where enterprises are expanding digital infrastructure and looking for lower-cost ways to support local AI execution.

Europe remains a major region in the AI framework optimization market because regulation now shapes deployment design as much as performance does. The EU AI Act, which became fully applicable from August 2, 2026, increases the value of auditable optimization workflows for high-risk systems. Germany, the United Kingdom, and France form the main demand centers through manufacturing, financial services, healthcare, and public-sector use cases that require reliable inference behavior. The European Commission's June 2026 proposal for the Cloud and AI Development Act also points to stronger sovereign compute frameworks, which can support on-premises and hybrid stack adoption across Europe and influence buyer priorities in nearby regulated markets.

Asia-Pacific is projected to expand at 27.42% CAGR through 2031, making it the fastest-growing regional block in the AI framework optimization market. Growth in the region is supported by government-backed AI infrastructure plans, very large device manufacturing bases, and stronger interest in domestic software ecosystems. China, India, Japan, and South Korea each contribute in different ways, with China emphasizing self-reliance, India widening access to compute, Japan linking AI investment to industrial modernization, and South Korea supporting hardware and device ecosystems. Southeast Asia adds momentum because enterprises in Indonesia, Malaysia, and Vietnam are moving from experimentation toward more stable operational deployment. Middle East and Africa also show rising activity as sovereign AI programs and local data initiatives increase interest in optimization software that can work across cloud, private, and edge environments.

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Alphabet Inc.
5. Microsoft Corporation
6. Amazon Web Services, Inc.
7. IBM Corporation
8. Oracle Corporation
9. Meta Platforms, Inc.
10. Qualcomm Incorporated
11. Graphcore Limited
12. Cerebras Systems, Inc.
13. Hugging Face, Inc.
14. Groq, Inc.
15. Modular, Inc.
16. Hailo Technologies Ltd.
17. SambaNova Systems, Inc.
18. Red Hat, Inc.
19. Hewlett Packard Enterprise Company
20. Google DeepMind

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 LANDSCAPE

• 4.1 Market Overview
• 4.2 Impact of Macroeconomic Factors on the Market
• 4.3 Market Drivers
  • 4.3.1 Rising Demand for Ultra-Low Latency Inferencing
  • 4.3.2 Expansion of Edge AI and On-Device Intelligence
  • 4.3.3 Rising Enterprise Spending on AI Runtime Efficiency
  • 4.3.4 Growth of Generative AI and Agentic Workflows
  • 4.3.5 Hardware-Agnostic Optimization and Interoperability Needs
  • 4.3.6 Data Sovereignty and Privacy-First AI Deployment
• 4.4 Market Restraints
  • 4.4.1 High Cost of Specialized AI Infrastructure
  • 4.4.2 Framework Fragmentation and Integration Complexity
  • 4.4.3 Shortage of AI Optimization and Systems Talent
  • 4.4.4 Model Accuracy Trade-Offs During Compression
• 4.5 Industry Value Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Solution Type
  • 5.1.1 Model Optimization and Compression Software
  • 5.1.2 Graph Compilation and Kernel Optimization Software
  • 5.1.3 AI Runtime and Hardware Acceleration Software
  • 5.1.4 AI Inference Serving and Orchestration Software
  • 5.1.5 Performance Profiling, Benchmarking, and Observability Tools
  • 5.1.6 Professional and Managed Optimization Services
• 5.2 By Deployment Environment
  • 5.2.1 Cloud and Hyperscale Data Centers
  • 5.2.2 On-Premises and Private Cloud
  • 5.2.3 Edge Infrastructure
  • 5.2.4 On-Device AI
  • 5.2.5 Hybrid Deployment
• 5.3 By Organization Size
  • 5.3.1 Large Enterprises
  • 5.3.2 Small and Medium Enterprises
• 5.4 By Application
  • 5.4.1 Generative AI, Large Language Models, and Multimodal AI
  • 5.4.2 Natural Language Processing and Document Intelligence
  • 5.4.3 Computer Vision and Video Analytics
  • 5.4.4 Speech and Audio AI
  • 5.4.5 Recommendation, Search, and Personalization Engines
  • 5.4.6 Predictive Analytics, Classical ML, and Decision Intelligence
  • 5.4.7 Robotics, Autonomous Systems, and Edge Intelligence
  • 5.4.8 Other Applications
• 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 Market 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 Alphabet Inc.
  • 6.4.5 Microsoft Corporation
  • 6.4.6 Amazon Web Services, Inc.
  • 6.4.7 IBM Corporation
  • 6.4.8 Oracle Corporation
  • 6.4.9 Meta Platforms, Inc.
  • 6.4.10 Qualcomm Incorporated
  • 6.4.11 Graphcore Limited
  • 6.4.12 Cerebras Systems, Inc.
  • 6.4.13 Hugging Face, Inc.
  • 6.4.14 Groq, Inc.
  • 6.4.15 Modular, Inc.
  • 6.4.16 Hailo Technologies Ltd.
  • 6.4.17 SambaNova Systems, Inc.
  • 6.4.18 Red Hat, Inc.
  • 6.4.19 Hewlett Packard Enterprise Company
  • 6.4.20 Google DeepMind

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment