Artificial Intelligence (AI) Chip Market (2035): GPUs, Custom AI Silicon & Data Center AI Accelerators Growth in United States Expansion

DataM Intelligence has released a new research report titled "Artificial Intelligence (AI) Chip Market Size 2026." The report provides insights into market trends, segmentation, regional growth, CAGR projections, and key industry players. It also examines major growth drivers, including generative AI adoption, expanding data-center infrastructure, custom AI chips, edge AI, and rising demand for high-performance computing, along with the market outlook through 2035.

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Recent Key Developments - United States & North America
September 2026
Qualcomm and Amazon sign a multi-generational AI chip partnership
Qualcomm and Amazon announced a long-term collaboration on September 8 to develop next-generation AI data-center infrastructure. The agreement includes a potential purchase of up to US$60 billion in AI data-center chips and related products, alongside work on optical connectivity and custom silicon. The deal gives Qualcomm a larger role in cloud AI infrastructure beyond smartphones.

September 2026
OpenAI moves its custom AI chip into a broader production strategy
OpenAI disclosed progress on its Jalapeño AI processor, developed with Broadcom and manufactured by TSMC. The company says its first-generation inference accelerator was designed and brought to tape-out in nine months using OpenAI models. The move points to a shift toward vertically integrated AI infrastructure, where model developers also shape their own silicon.

July 2026
Meta prepares its next custom AI chip for production
Meta plans to put its Iris AI chip into production in September 2026 as part of its MTIA program. The chip was developed with Broadcom and fabricated by TSMC, with testing reportedly completed in six weeks. Meta's roadmap calls for a new chip every six months through 2027, reinforcing the move toward internally designed training and inference accelerators.

July 2026
U.S. advanced packaging becomes a key focus for AI chip scaling
Intel highlighted Foveros stacking, EMIB silicon bridges, and EMIB-T technology in its July 29 update on advanced packaging. These approaches connect multiple chiplets into larger computing systems and help address the limits of single-die designs. The development reflects a shift in AI chip engineering toward packaging and interconnects, not just transistor improvements.

Recent Key Developments - Japan & Asia-Pacific
September 2026
OpenAI and Samsung deepen cooperation on next-generation chips
OpenAI and Samsung are working together on next-generation chip development and enterprise AI services, according to a September 9 report. The collaboration builds on existing relationships involving Samsung and SK Hynix, including memory supply for OpenAI's Stargate data-center infrastructure. This connects AI accelerator development with the region's advanced memory manufacturing capabilities.

August 2026
India expands subsidized AI compute and indigenous chip development
India's Ministry of Electronics and Information Technology reported in August that the IndiaAI Mission had expanded shared compute capacity to more than 45,000 GPUs by June 2026. The government also approved Semicon 2.0 in July, with an outlay of ₹1,27,500 crore to support chip design, fabrication, advanced packaging, and the wider semiconductor ecosystem.

August 2026
Qualcomm expands its AI hardware presence in Japan
Qualcomm announced a robotics investment initiative in Japan on August 25, anchored by a new Qualcomm Japan Robotics Center. The move supports development around robotics and intelligent edge systems, extending Qualcomm's AI chip strategy into industrial and physical AI applications.

List of Key Players 2026:
NVIDIA Corporation, Intel Corporation, Advanced Micro Devices (AMD), MediaTek Inc., Google, Samsung Electronics, Qualcomm Technologies, Alphabet Inc., IBM and Cambricon Technologies

Company Evaluation
Leading players such as NVIDIA, AMD and Intel are strengthening their AI chip portfolios through GPUs, AI accelerators, data-center processors, AI PCs and edge computing solutions. Google, Samsung, MediaTek and Qualcomm are advancing AI chips for cloud, smartphones, PCs and edge applications, while Cambricon Technologies focuses on dedicated AI acceleration solutions. Competition is increasingly driven by performance, energy efficiency, memory bandwidth and software ecosystems.

Growth Forecast Projected 2026:
The Global Artificial Intelligence Chip Market is projected to reach US$361.14 billion by 2035, expanding at a CAGR of 16.64% during 2026-2035. Growth is supported by rising generative AI adoption, expanding data centers, increasing AI training and inference workloads, and growing integration of AI into edge devices, PCs, smartphones, automotive systems and robotics.

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M&A / Strategic Activity
Recent strategic partnerships, acquisitions, and ecosystem developments shaping the Artificial Intelligence Chip Market:

Qualcomm - Amazon: Custom AI chips and optical networking
September 2026: Qualcomm and Amazon announced a multigenerational agreement covering customized AI chips for Amazon's data centers. The partnership also includes optical connectivity technology and gives Qualcomm a route into large-scale cloud AI infrastructure. Qualcomm's acquisition of AlphaWave Semi in 2025 supports its optical networking ambitions.

OpenAI - Samsung Electronics: Next-generation chip collaboration
September 2026: OpenAI and Samsung Electronics are working together on next-generation chip development. The collaboration builds on their broader relationship, including Samsung's role in AI memory supply for OpenAI's Stargate infrastructure. It reflects a move toward closer coordination between AI model developers, chip designers, and memory manufacturers.

NVIDIA - d-Matrix: Integrating inference chips into AI server systems
September 2026: d-Matrix announced plans to integrate its Raptor inference processors into NVIDIA data center systems using NVLink Fusion. The arrangement targets AI inference workloads, including chatbots, coding assistants, and voice agents. The Raptor integration is expected to become available in 2027, subject to the development roadmap.

NVIDIA - CoreWeave: Early access to Rubin AI infrastructure
January 2026: CoreWeave was named among the first providers to offer NVIDIA Rubin systems through its cloud infrastructure. The collaboration brings NVIDIA's next-generation rack-scale AI platform into a cloud delivery model, giving customers access to high-performance AI computing without building the entire infrastructure themselves.

AMD - Open AI ecosystem partnerships
June 2025: AMD announced partnerships and ecosystem collaborations involving Meta, OpenAI, Oracle, Microsoft, Cohere, HUMAIN, Red Hat, Astera Labs, and Marvell at its Advancing AI 2025 event. The initiative centers on Instinct accelerators, ROCm software, and open-standard rack-scale AI systems, with Oracle Cloud Infrastructure among the early deployment platforms.

New Product/Chip Launches & Deployments
Recent AI chip launches and infrastructure deployments:
NVIDIA - Rubin AI platform
January 2026: NVIDIA introduced the Rubin platform, bringing together the Vera CPU, Rubin GPU, NVLink 6 switch, ConnectX-9 SuperNIC, BlueField-4 DPU, and Spectrum-6 Ethernet switch. The platform is designed around coordinated hardware and software for large-scale AI training and inference. NVIDIA reported up to a 10x reduction in inference token cost compared with Blackwell for the specified workloads.

NVIDIA - Vera Rubin and Groq 3 LPX integration
March 2026: NVIDIA announced the Vera Rubin platform with seven chips in full production, including the integration of the NVIDIA Groq 3 LPU for inference. The platform includes dedicated systems for GPU compute, networking, storage, and inference, reflecting a more specialized approach to AI infrastructure.
Google - Ironwood TPU
April 2025 / November 2025: Google introduced Ironwood, its seventh-generation TPU, designed specifically for inference. Google subsequently made Ironwood available to Cloud customers, with the system supporting up to 9,216 chips in a superpod. The deployment is focused on high-volume, low-latency AI model serving.

AMD - Instinct MI350 Series
June 2025: AMD launched the Instinct MI350 Series, based on its fourth-generation CDNA architecture. The MI350X and MI355X accelerators offer up to 288 GB of HBM3E memory and 8 TB/s of peak theoretical memory bandwidth. AMD also introduced ROCm 7.0 and previewed the Helios rack-scale AI platform.

Intel - Gaudi 3 AI accelerators
2025-2026 deployment: Intel's Gaudi 3 portfolio includes PCIe accelerators designed for enterprise AI workloads, including large language models, multimodal AI, and retrieval-augmented generation. The platform uses standard Ethernet networking and is positioned as an alternative to proprietary AI interconnect architectures.

R&D & Technological Advancements
Inference-first AI chip architecture
AI chip design is moving beyond training-only performance toward the economics of inference. Google's Ironwood is explicitly built for inference, while NVIDIA's Rubin platform and AMD's newer accelerators target sustained AI serving, reasoning, and high-throughput workloads. This changes the design priorities from raw training speed alone to memory capacity, bandwidth, interconnects, and cost per token.

HBM4 and high-bandwidth memory
NVIDIA's Rubin GPU architecture uses HBM4 memory, with NVIDIA reporting up to 288 GB capacity and 22 TB/s bandwidth. These specifications support larger models and high-concurrency inference while reducing dependence on moving data between processor and external memory.

Low-precision AI computing
AMD's MI350 Series supports expanded AI data types, including MXFP6 and MXFP4. NVIDIA's Rubin architecture also introduces NVFP4 capabilities. The focus is on delivering more AI computations per unit of hardware and energy while maintaining the accuracy needed for practical model workloads.

Open Ethernet-based AI infrastructure
Intel Gaudi 3 is designed around standard Ethernet and RoCE networking, while AMD is developing open-standard rack-scale systems using its Infinity Fabric and networking portfolio. These approaches are intended to give cloud operators and enterprises more flexibility in assembling AI clusters rather than relying on a single proprietary networking architecture.

Optical connectivity for AI data centers
Qualcomm's September 2026 agreement with Amazon includes optical connectivity technologies and optical DSP development. This is relevant to AI chip infrastructure because large AI clusters require high-bandwidth links between computing, memory, and networking systems.

Chiplet and rack-scale co-design
NVIDIA's Rubin platform combines multiple specialized chips, including CPUs, GPUs, switches, DPUs, and SuperNICs, into a coordinated AI system. AMD's Helios roadmap similarly combines GPUs, CPUs, and networking components in a rack-scale design. This reflects a shift from optimizing individual accelerators to designing the complete AI computing system

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Segments Covered in the Global Artificial Intelligence (AI) Chip Market:
By Chip
The market is segmented into GPU, ASIC, FPGA, CPU, and others. GPUs dominate due to their parallel processing capabilities for AI training and deep learning. ASICs are witnessing strong growth driven by AI inference, customized acceleration, and energy-efficient data centers. FPGAs support edge computing and real-time processing, while CPUs serve general-purpose AI workloads.

By Processing
The market is divided into Cloud and Edge. Cloud processing dominates due to the adoption of cloud-based AI platforms, large-scale model training, and high-performance computing. Edge processing is growing with demand for real-time inference, low-latency decision-making, and localized processing across smart devices, industrial automation, and IoT.

By Technology
The market is segmented into SoC, SiP, MCM, and others. SoC dominates due to its integrated processing and AI acceleration capabilities. SiP is witnessing strong growth driven by advanced packaging and compact AI-enabled devices. MCM supports data centers, AI accelerators, and high-performance computing systems.

By Application
The market is divided into NLP, Robotics, Computer Vision, Network Security, and others. NLP dominates due to the adoption of generative AI, language models, and conversational AI. Robotics is growing with AI-powered automation in manufacturing, logistics, and healthcare. Computer Vision and Network Security support image recognition, quality inspection, and AI-powered threat detection.

By Industry Vertical
The market is segmented into Media and Advertising, BFSI, IT and Telecom, Retail, Healthcare, Automotive and Transportation, and others. Media and Advertising dominates due to AI-powered content creation, personalized advertising, and recommendation systems. BFSI is witnessing strong growth in fraud detection, risk assessment, and intelligent customer service, while IT, healthcare, and automotive expand AI chip adoption.

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