GPU 프로그래밍 플랫폼 : 시장 점유율 분석, 업계 동향과 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the GPU programming platform market size was USD 4.73 billion in 2025 and is projected to reach USD 15.97 billion by 2031, expanding at a CAGR of 22.20% over 2026-2031.

This report is Segmented by Component (Software, and Services), Deployment Model (On-Premises, Public Cloud, Private Cloud/Dedicated Hosted Cloud, and More), Programming Model (ROCm and HIP, Oneapi and SYCL, Opencl, Directive-Based Models, GPU Kernel and AI Compiler Toolchains, and More), End User (Telecommunications, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global GPU Programming Platform Market Trends and Insights

Rising AI Training And Inference Workloads Requiring Portable GPU Code

AI model development has shifted the GPU programming platform market from a specialized niche to a mainstream software requirement for enterprise compute. NVIDIA reported that Blackwell-class systems led MLPerf Training v6.0 at scales up to 8,192 GPUs, which shows how much software performance now matters when large training clusters are deployed in production. The broader expansion is driven by inference, because production inference must run efficiently across a mix of cloud instances, local clusters, and edge systems rather than a single uniform training environment. That need is pushing the GPU programming platform market toward middleware, SDKs, and abstraction tools that can manage different memory structures, compiler behaviors, and precision settings without forcing teams to rebuild applications from scratch. The U.S. Department of Energy also noted in 2026 that AI-generated HPC code was reaching high levels of trust across key scientific domains, which supports demand for platforms that can serve both AI and scientific computing on shared GPU estates.

Growing Enterprise Demand for Cross-Vendor GPU Portability

Enterprise buyers are increasingly seeking software that reduces reliance on a single GPU vendor, especially when supply concentration and hardware pricing can influence deployment decisions. The GPU programming platform market is responding by shifting more value into portability layers that help workloads run across NVIDIA and AMD environments with fewer code changes. AMD highlighted wider ROCm adoption and broader platform support in early 2026, showing that alternative software stacks are becoming more practical for production AI and HPC use cases. Modular reinforced that direction in April 2026, when it enabled a single container to run across NVIDIA and AMD Instinct GPUs, giving enterprises a direct path to multi-vendor deployment without rewriting applications at each hardware transition. This matters because the GPU programming platform market grows faster when software decisions are no longer tied to a single hardware roadmap and when procurement teams can spread workloads across multiple suppliers.

Deep CUDA Ecosystem Lock-In and Migration Friction

CUDA remains the strongest structural restraint on broad diversification in the GPU programming platform market because many production workloads were built, tested, and optimized first for NVIDIA environments. NVIDIA continues to deepen that position through CUDA-X libraries, compiler improvements, and developer tooling that shorten the optimization workload for teams already in the NVIDIA ecosystem. In practical terms, enterprises with large AI and HPC codebases face a long migration path because custom kernels, framework integrations, and memory management choices often require separate validation when moving to another stack. The problem is more pronounced in safety-sensitive, regulated workloads, where the validation history of an existing CUDA-based implementation can slow the adoption of alternatives. This keeps parts of the GPU programming platform market tied to incumbent environments, even when competing hardware or open stacks become more capable.

Other drivers and restraints analyzed in the detailed report include:

1. Expansion of Cloud-Native GPU Development Environments
2. Open-Source GPU Toolchains Lowering Entry Barriers for New Users
3. Fragmented Standards Across HIP, SYCL, oneAPI, and OpenCL

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

Segment Analysis

Software accounted for 62.38% of the GPU programming platform market in 2025 and is projected to expand at a 23.41% CAGR through 2031, indicating that value is moving toward the development and execution layers rather than remaining concentrated on hardware access alone. That position reflects the importance of programming models, compilers, middleware, profiling tools, and SDKs in making GPU workloads usable across AI training, inference, and scientific computing. NVIDIA reinforced this direction in 2026 with CUDA 13.3 and CompileIQ, which introduced AI-driven compiler autotuning and tile-based C++ kernel programming to improve optimization productivity on production workloads. As the GPU programming platform market expands, software continues to attract the strongest spending because enterprises need portability, monitoring, and faster tuning cycles more than a one-time infrastructure setup.

Services represented the remaining 37.62% share in 2025, and this part of the GPU programming platform industry is gaining weight as deployments become more complex and migration projects multiply. Consulting, integration, and code porting services are benefiting from demand to move workloads between CUDA, ROCm, and SYCL environments without disrupting production performance. Meta's KernelAgent project, which focuses on LLM-assisted Triton kernel generation across NVIDIA and Intel XPU targets, also points to a growing need for training, implementation support, and structured developer enablement as toolchains become more automated. Managed services are likely to remain important for mid-sized enterprises that do not want to build in-house compiler and performance engineering teams, which gives the GPU programming platform market a durable services tail alongside software licensing and platform subscriptions.

Public cloud held 46.51% share of the GPU programming platform market size in 2025, reflecting the scale advantage hyperscalers have in managed GPU environments, elastic capacity, and integrated development services. The leading cloud position was built on broad support from AWS, Google Cloud, Microsoft Azure, Oracle, and GPU-focused providers that package compute with orchestration and access to frameworks. VAST Data's Polaris release in February 2026 captured that shift by offering orchestration across public cloud, neocloud, and on-premises environments through one control plane. At the same time, hybrid and multicloud are projected to expand at a 22.73% CAGR through 2031, as many enterprises seek to keep regulated data and persistent inference workloads closer to their internal infrastructure while using external capacity for peak demand.

Private cloud, dedicated hosted cloud, and on-premises estates remain important where data residency, security, or stable utilization levels justify tighter infrastructure control. Germany's Industrial AI Cloud, launched in February 2026 with around 10,000 NVIDIA Blackwell GPUs, demonstrated that sovereign and regulated deployments can still connect to large-scale AI programs without relying solely on public cloud architectures. On-premises relevance is also visible in scientific environments such as the DOE's NERSC Doudna system, where next-generation supercomputing still depends on a local programming environment that supports the same software libraries used in cloud AI pipelines. This mix supports the GPU programming platform market because customers are not choosing one deployment model over another, they are asking for software continuity across all of them.

Complete Report Scope:

• By Component
  • Software
    • Programming Tools and Compilers
    • Middleware, SDKs, and Portability Tools
    • Libraries and Runtime Systems
    • Performance Monitoring and Profiling Tools
    • Developer, Testing, and Debugging Tools
  • Services
    • Consulting, Integration, and Code Migration Services
    • Managed Services
    • Training, Support, and Maintenance Services
• By Deployment Model
  • On-Premises
  • Public Cloud
  • Private Cloud / Dedicated Hosted Cloud
  • Hybrid and Multicloud
• By Programming Model
  • CUDA
  • ROCm and HIP
  • oneAPI and SYCL
  • OpenCL
  • Directive-Based Models
  • GPU Kernel and AI Compiler Toolchains
  • Other Programming Models
• By End User
  • Cloud Service Providers and Data Center Operators
  • IT, Software, Internet, and SaaS Providers
  • Telecommunications
  • Banking, Financial Services, and Insurance
  • Healthcare and Life Sciences
  • Manufacturing
  • Automotive and Transportation
  • Other End Users
• 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 held 51.82% of the GPU programming platform market share in 2025, which kept it firmly ahead of every other region. The region combines the largest concentration of GPU vendors, hyperscalers, AI software companies, and enterprise buyers, providing a strong base for both platform development and adoption. CoreWeave's funding activity in 2026 and its USD 6 billion agreement with Jane Street showed that commercial demand for GPU infrastructure was still scaling rapidly in the region. NVIDIA's September 2025 collaboration with Intel also reflected the depth of the North American supply chain and platform coordination around AI infrastructure. In addition, the U.S. Department of Energy continued to support code portability and abstraction work for advanced computing, which helps sustain long-term software demand around heterogeneous GPU environments.

Europe remains a structurally important region for the GPU programming platform market because data sovereignty, industrial policy, and regulated AI deployment are shaping demand. The EU planned a portfolio of up to 5 AI Gigafactories, with the first facilities expected to become operational from 2026, which supports a new wave of sovereign GPU software environments tied to public and industrial investment ZDF. Germany's Industrial AI Cloud, developed with Deutsche Telekom, NVIDIA, and Polarise, added one of the clearest examples of this model in February 2026 through a large Blackwell-based deployment. This environment favors software stacks that can combine compliance, performance tracking, and deployment flexibility across private and connected cloud resources.

Asia-Pacific is projected to expand at a 22.68% CAGR through 2031, which makes it the fastest-growing regional block in the GPU programming platform market. The growth is tied to sovereign compute buildouts and domestic ecosystem development, especially in China and India, where GPU infrastructure strategy is becoming part of broader AI capacity planning. The region also benefits from growing acceptance of open and multi-vendor toolchains as enterprises prepare for mixed GPU fleets instead of one uniform hardware base. South America and the Middle East and Africa remain earlier-stage markets, but the infrastructure conditions for adoption are improving as hyperscalers and regional GPU cloud providers widen access to advanced compute. As that access improves, the GPU programming platform market should broaden across finance, manufacturing, and telecom workloads in these regions as well.

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Amazon Web Services, Inc.
5. Microsoft Corporation
6. IBM Corporation
7. Google LLC
8. Hewlett Packard Enterprise Development LP
9. Dell Technologies Inc.
10. Alibaba Group Holding Limited
11. Tencent Holdings Limited
12. Red Hat, Inc.
13. DigitalOcean Holdings, Inc.
14. CoreWeave, Inc.
15. Anyscale, Inc.
16. Modular, Inc.
17. Scale AI, Inc.
18. SchedMD LLC
19. Atos SE

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 Market Drivers
  • 4.2.1 Rising AI Training and Inference Workloads Requiring Portable GPU Code
  • 4.2.2 Growing Enterprise Demand for Cross-Vendor GPU Portability
  • 4.2.3 Expansion of Cloud-Native GPU Development Environments
  • 4.2.4 Open-Source GPU Toolchains Lowering Entry Barriers for New Users
  • 4.2.5 Increasing Use of Heterogeneous Compute in HPC and GenAI Stacks
  • 4.2.6 Rising Need for Performance Tuning and Developer Productivity Tools
• 4.3 Market Restraints
  • 4.3.1 Deep CUDA Ecosystem Lock-In and Migration Friction
  • 4.3.2 Fragmented Standards Across HIP, SYCL, oneAPI, and OpenCL
  • 4.3.3 High Validation, Re-Optimization, and Testing Costs During Porting
  • 4.3.4 Scarcity of GPU Compiler and Performance Engineering Talent
• 4.4 Industry Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Software
    • 5.1.1.1 Programming Tools and Compilers
    • 5.1.1.2 Middleware, SDKs, and Portability Tools
    • 5.1.1.3 Libraries and Runtime Systems
    • 5.1.1.4 Performance Monitoring and Profiling Tools
    • 5.1.1.5 Developer, Testing, and Debugging Tools
  • 5.1.2 Services
    • 5.1.2.1 Consulting, Integration, and Code Migration Services
    • 5.1.2.2 Managed Services
    • 5.1.2.3 Training, Support, and Maintenance Services
• 5.2 By Deployment Model
  • 5.2.1 On-Premises
  • 5.2.2 Public Cloud
  • 5.2.3 Private Cloud / Dedicated Hosted Cloud
  • 5.2.4 Hybrid and Multicloud
• 5.3 By Programming Model
  • 5.3.1 CUDA
  • 5.3.2 ROCm and HIP
  • 5.3.3 oneAPI and SYCL
  • 5.3.4 OpenCL
  • 5.3.5 Directive-Based Models
  • 5.3.6 GPU Kernel and AI Compiler Toolchains
  • 5.3.7 Other Programming Models
• 5.4 By End User
  • 5.4.1 Cloud Service Providers and Data Center Operators
  • 5.4.2 IT, Software, Internet, and SaaS Providers
  • 5.4.3 Telecommunications
  • 5.4.4 Banking, Financial Services, and Insurance
  • 5.4.5 Healthcare and Life Sciences
  • 5.4.6 Manufacturing
  • 5.4.7 Automotive and Transportation
  • 5.4.8 Other End Users
• 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 Amazon Web Services, Inc.
  • 6.4.5 Microsoft Corporation
  • 6.4.6 IBM Corporation
  • 6.4.7 Google LLC
  • 6.4.8 Hewlett Packard Enterprise Development LP
  • 6.4.9 Dell Technologies Inc.
  • 6.4.10 Alibaba Group Holding Limited
  • 6.4.11 Tencent Holdings Limited
  • 6.4.12 Red Hat, Inc.
  • 6.4.13 DigitalOcean Holdings, Inc.
  • 6.4.14 CoreWeave, Inc.
  • 6.4.15 Anyscale, Inc.
  • 6.4.16 Modular, Inc.
  • 6.4.17 Scale AI, Inc.
  • 6.4.18 SchedMD LLC
  • 6.4.19 Atos SE

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment