By 2030, IDTechEx forecasts that the deployment of AI data centres, commercialisation of AI, and the increasing performance requirements from large AI models will perpetuate the already soaring market size of AI chips to over $400-billion.
However, the underlying technology must evolve to remain competitive with the demand for more efficient computation, lower costs, higher performance, massively scalable systems, faster inference, and domain-specific computation.
IDTechEx’s latest report – AI chips for Data Centres and Cloud 2025-2035: Technologies, Market, Forecasts – characterises data centre and cloud AI chip technologies, key players, and markets. This report provides market intelligence for the AI chips space, with coverage across AI chip types including graphics processing units (GPUs), AI-capable central processing units (CPUs), custom AI application-specific integrated circuits (ASICs), and other AI chips, spanning over 50 industry players, and with market forecasts from 2025 to 2035.
The opportunity for AI chips
Frontier artificial intelligence (AI) has persistently attracted hundreds of billions in global investment year-on-year with governments and hyperscalers racing to lead in domains like drug discovery and autonomous infrastructure. Graphics processing units (GPUs) and other AI chips have been instrumental in driving the growth in performance of top AI systems, providing the compute needed for deep learning within data centres and cloud infrastructure. However, with the capacity of global data centres expected to reach hundreds of GWs in the coming years – and investments reaching hundreds of billions of dollars – concerns about the energy efficiency and costs of current hardware have increasingly come into the spotlight.
Graphics Processing Units (GPUs) currently dominate the AI chips market
The largest systems for AI are massive scale-out HPC and AI systems – these heavily implement GPUs. These tend to be hyperscaler AI data centres and supercomputers, both of which can offer exaFLOPS of performance, on-premises or over distributed networks. Nvidia has seen remarkable success over recent years with its Hopper (H100/H200) chips and recently released Blackwell (B200/B300) chips. AMD has also created competitive chips with its MI300 series processors (MI300X/MI325X). Over the last few years, Chinese players have also been developing solutions due to sanctions from the US on advanced chips which prevent the export of US-based chips to China.
These high-performance GPUs continue to adopt the most advanced semiconductor technologies, which are explored in detail in the new IDTechEx report. In-depth examination of these technologies such as high-bandwidth memory (HBM) from memory providers (eg. Samsung, SK Hynix, and Micron Technology) and advanced semiconductor 2.5D and 3D packaging, transistor technologies, and chiplet technologies from foundries and IDMs (eg. TSMC, Samsung Foundry, and Intel Foundry) can be found within the report.
Custom AI chips used by hyperscalers and Cloud Service Providers (CSPs)
High-performance GPUs have been integral for training AI models, however, they do face various limitations. These include high total cost of ownership (TCO), vendor lock-in risks, low utilisation for AI-specific operations, and can be overkill for specific inference workloads. Because of this, an emerging strategy used by hyperscalers is to adopt custom AI ASICs from ASIC designers such as Broadcom and Marvell.
These custom AI ASICs have purpose-built cores for AI workloads, are cheaper per operation, are specialised for particular systems (eg. transformers, recommender systems, etc.), and offer energy-efficient inference. These also give hyperscalers and CSPs the opportunity for full-stack control and differentiation without sacrificing performance. Evaluation of potential risks, key partnerships, player activity, benchmarking, and technology overviews are available within the report.
Emergence of other AI chips for data centre and cloud
Both large vendors and AI chip-specific startups have released alternative AI chips which offer benefits over the incumbent GPU technologies. These are designed using similar and novel AI chip architectures, intending to make more suitable chips for AI workloads, targeted at lowering costs and more efficient AI computations. Some large chip vendors such as Intel, Huawei, and Qualcomm have designed AI accelerators (eg. Gaudi, Ascend 910, Cloud AI 100) using heterogeneous arrays of compute units (similar to GPUs), but purpose-built to accelerate AI workloads. These offer a balance between performance, power efficiency, and flexibility for specific application domains.
AI chip-focused startups often take a different approach, deploying cutting-edge architectures and fabrication techniques with the likes of dataflow-controlled processors, wafer-scale packaging, spatial AI accelerators, processing-in-memory (PIM) technologies, and coarse-grained reconfigurable arrays (CGRAs). Various companies have successfully launched these systems (Cerebras, Groq, Graphcore, SambaNova, Untether AI, and others) for data centres and cloud computing.
These systems perform exceptionally, especially in scale-up environments, but may struggle in massive scale-out environments – especially when compared to high-performance GPUs. IDTechEx’s report offers comprehensive benchmarking, comparisons, key trends, technology breakdowns, challenges, and player activity.
The various technologies involved in designing and manufacturing give a wide breadth for future technological innovation across the semiconductor industry supply chain. Government policy and heavy investment show the prevalent interest in pushing frontier AI toward new heights and this will require exceptional volumes of AI chips within AI data centres to meet this demand.
IDTechEx forecasts that the AI Chips market will reach $453-billion by 2030 at a CAGR of 14% between 2025 and 2030.