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Global AI Inference Chip Market Size, Share, Trends and Forecast 2027-2037 | Token Economics, Custom ASICs and Edge AI Create Strategic Growth Opportunities

globenewswire.com
NVDA NVIDIA is mentioned in the context of its inference position and response within the AI inference chip market, indicating its significant role and ongoing strategic considerations in this sector. AMD AMD is listed as a diversified vendor in the AI inference chip market, suggesting its involvement and competitive presence in this evolving technology landscape. INTC Intel is identified as a diversified vendor in the AI inference chip market, indicating its participation and strategic positioning within this sector. QCOM Qualcomm is mentioned as a diversified vendor in the AI inference chip market, highlighting its role and presence in the competitive landscape.

Global AI Inference Chip Market Size, Share, Trends and Forecast 2027-2037 | Token Economics, Custom ASICs and Edge AI Create Strategic Growth Opportunities Dublin, Nov. 02, 2026 (GLOBE NEWSWIRE) -- "The Global AI Inference Chip Market 2027-2037" has been added to ResearchAndMarkets.com's offering.

Investment in AI inference silicon has emerged as one of the semiconductor industry's largest capital flows as buyers increasingly prioritize cost per token, power efficiency and deployment economics over training speed. The report delivers an in-depth analysis of the global AI inference chip market, covering technologies, commercial models, competitive dynamics and supply chain developments. It includes market sizing and forecasts from 2024 to 2037, supported by profiles of 125 companies operating across the AI hardware ecosystem.

The report examines record venture funding activity during 2025-2026, including major financing rounds, mergers and acquisitions, and exits such as the Cerebras IPO. Its technology coverage spans data-centre GPUs, hyperscaler custom ASICs, merchant and challenger accelerators, in-memory compute, photonic inference, neuromorphic inference and edge NPUs. Analysis focuses on the factors reshaping the market, including rapid token-demand growth, falling inference costs, the memory wall, power and siting constraints, sovereign AI investment and export controls.

Key areas covered in the report include:

The report concludes with a market outlook to 2037, identifying commercial opportunities, technology risks and strategic recommendations for semiconductor companies, cloud providers, investors, enterprises and policymakers. Company profiles, research methodology, supporting references and market data provide additional insight into the businesses and technologies influencing the future of AI inference hardware.

Key Topics Covered

1 EXECUTIVE SUMMARY

1.1 The Inference Decade: Why the Market Is Changing Now

1.2 Market Definition and Scope at a Glance

1.3 Global Market Size and Growth, 2024-2037

1.4 Data Center Versus Edge

1.5 Chip Architecture Shift: GPUs, Custom ASICs and Challengers

1.6 Competitive Landscape in Brief

1.7 Fastest-Growing Segments

1.8 Capital Formation and Exits

1.9 Ten Predictions for 2027-2037

1.10 Strategic Implications by Stakeholder

2 INTRODUCTION, DEFINITIONS AND SEGMENTATION

2.1 Training Versus Inference

2.2 Anatomy of LLM Inference: Prefill, Decode and the KV Cache

2.3 Inference Performance and Economic Metrics

2.4 Market Definition and What Is Counted

2.5 Segmentation Framework

2.6 Taxonomy of Inference Silicon

2.7 A Short History of Inference Chips, 2015-2026

3 MARKET DRIVERS, TRENDS AND RESTRAINTS

3.1 Token Demand Explosion: Agentic AI, Reasoning Models and Multimodal

3.2 The Economics of Inference: Falling Cost per Token

3.3 The Memory Wall and the Rise of Memory-Centric Design

3.4 Power, Grid and Siting Constraints

3.5 Disaggregated and Heterogeneous Inference

3.6 Software Ecosystems and Switching Costs

3.7 Sovereign AI and National Compute Programs

3.8 On-Device and Edge AI Demand Drivers

3.9 Geopolitics, Export Controls and the US-China Silicon Curtain

3.10 Restraints, Risks and Challenges

4 TECHNOLOGY ANALYSIS

4.1 GPUs for Inference

4.2 Hyperscaler Custom ASICs

4.3 Merchant Data-Center Inference ASICs: Dataflow, Deterministic SRAM (LPU), Wafer-Scale, Transformer-Specific and Hard-Wired

4.4 Memory-Centric Architectures: SRAM-Centric, Digital and Analog In-Memory Compute, PIM

4.5 Numerics: FP8, MXFP4/NVFP4, INT4, Ternary, Logarithmic

4.6 Photonic and Optical Inference Compute

4.7 Neuromorphic Inference

4.8 Edge NPUs and SoC Integration

4.9 Discrete Edge AI Accelerators

4.10 Memory Technologies for Inference: HBM3E, HBM4/HBM4E, Custom HBM, GDDR7, LPDDR5X/6, SOCAMM, HBF and CXL

4.11 Advanced Packaging and Chiplets: CoWoS, SoIC, Panel-Level and UCIe

4.12 Scale-Up and Scale-Out Interconnect: NVLink, UALink, Ethernet (UEC and ESUN) and Co-Packaged Optics

4.13 Process Technology: N3, N2, A16, Intel 18A/14A, Samsung SF2 and Rapidus

4.14 Rack-Scale Systems, Power Delivery and Cooling

4.15 Inference Software, Compilers and Serving Frameworks

4.16 Technology Readiness and Roadmap 2027-2037

5 GLOBAL MARKET FORECASTS 2024-2037

5.1 Forecast Methodology and Key Assumptions

5.2 Total Market

5.3 Data Center Inference by Chip Type

5.4 Data Center by Deployment Location

5.5 Data Center by Workload

5.6 Data Center by Memory Architecture

5.7 Data Center Unit Shipments and ASP

5.8 Edge and On-Device by Application

5.9 Edge Unit Shipments and Silicon Content

5.10 Market by Architecture

5.11 Segment Growth Summary

5.12 Scenario Analysis

5.13 Reconciliation with Other Future Markets Reports and Third-Party Estimates

6 DATA CENTER INFERENCE MARKET ANALYSIS

6.1 Hyperscaler Inference Strategies (Google, AWS, Microsoft, Meta, Oracle)

6.2 Frontier Labs as Silicon Buyers (OpenAI, Anthropic, xAI)

6.3 Neoclouds and Inference-as-a-Service

6.4 Sovereign and National AI Facilities

6.5 Enterprise On-Premise Inference

6.6 Total Cost of Ownership: GPU vs ASIC vs Challenger

6.7 Procurement Behavior and Contract Structures

7 EDGE AND ON-DEVICE INFERENCE MARKET ANALYSIS

7.1 Smartphones

7.2 AI PCs and Workstations

7.3 Automotive

7.4 Robotics and Physical AI

7.5 Industrial and Machine Vision

7.6 Smart Cameras, Video Analytics and Smart Cities

7.7 Wearables, XR, Smart Home and Consumer IoT

7.8 Edge Infrastructure: Telco MEC and On-Site Servers

7.9 Edge versus Cloud: Where Inference Will Run

8 REGIONAL MARKET ANALYSIS

8.1 Regional Overview

8.2 North America

8.3 China

8.4 Europe

8.5 Asia-Pacific (ex-China)

8.6 Middle East and Africa

8.7 Latin America

9 COMPETITIVE LANDSCAPE

9.1 Market Structure and Concentration

9.2 Market Shares by Vendor

9.3 NVIDIA's Inference Position and Response

9.4 AMD, Intel, Qualcomm and Other Diversified Vendors

9.5 Challenger Landscape by Segment

9.6 Strategic Partnerships and Ecosystems

9.7 Competitive Positioning Matrix

10 INVESTMENT, FUNDING, M&A AND EXITS

10.1 Venture Capital Deal Activity

10.2 Deal Sizes and Valuations

10.3 Capital Efficiency

10.4 Major Funding Rounds 2025-2026

10.5 Exits: IPOs and Acquisitions

10.6 Hyperscaler and Corporate Strategic Investment

10.7 Due Diligence Framework for Investors

11 SUPPLY CHAIN

11.1 Value Chain Overview

11.2 Foundry

11.3 Memory Suppliers

11.4 Advanced Packaging and OSAT

11.5 IP, EDA and Design Services

11.6 Systems and ODMs

11.7 Supply Chain Risks

12 MARKET OUTLOOK

12.1 Outlook 2027-2037

12.2 Key Risks

12.3 Opportunities

12.4 Recommendations

13 COMPANY PROFILES (125 company profiles)

14 RESEARCH METHODOLOGY

14.1 Objectives and Scope

14.2 Market Definition and Allocation Rules

14.3 Consistency with the Future Markets Catalogue

14.4 Primary and Secondary Research

14.5 Forecast Approach

14.6 Conventions and Limitations

15 GLOSSARY OF TERMS AND ABBREVIATIONS

16 REFERENCES

LIST OF TABLES [130]

LIST OF FIGURES [56]

A selection of companies mentioned in this report includes, but is not limited to:

For more information about this report visit https://www.researchandmarkets.com/r/ykb0k8

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