Meet the Data Centre Chips Powering the Internet

In 2025, the data centre industry is undergoing a transformative shift driven by advancements in chip technology. As AI workloads, cloud services, and real-time applications proliferate, hyperscalers such as Google Cloud, Microsoft Azure, AWS, and Nvidia are developing next-generation processors to meet the rising demands of the digital economy.

This article introduces the latest trends and new releases in data centre chip technologies, highlighting how these innovations are set to enhance performance, efficiency, and sustainability across the industry. By examining the current leading chip technologies, you will quickly gain a valuable understanding into how fast this sector of the industry is developing.

Chips in High Demand: Fueling Growth with Advanced Silicon

With AI models becoming more complex and cloud adoption accelerating, data centres require increasingly powerful and efficient processors. By 2028, server revenue is projected to more than double to $270 billion, underscoring the critical role of advanced silicon in digital infrastructure.

How These Chips Are Transforming Data Centres

• Boosting Compute Power: advanced chips like Nvidia’s H100 and AMD’s MI300 deliver exceptional processing capabilities, enabling faster AI model training and real-time analytics. Nvidia’s H100, based on the Hopper architecture, has become a benchmark for high-performance AI workloads, offering up to six times the performance of its predecessor, the A100.
• Driving Efficiency: by integrating co-processors such as Google’s TPUs and AWS’s Inferentia chips, data centres can offload specialised tasks, reducing the workload on primary CPUs. This not only improves energy efficiency, but also optimises overall resource utilisation.
• Specialised Delivery: custom-designed chips like Google Cloud’s TPU v5p and AWS’s Tranium2 cater to specific high-demand applications, including machine learning, video transcoding, and network function offloading. For example, Google’s TPUs have played a critical role in large-scale AI model training, powering services such as Google Translate and YouTube recommendations.

Rollouts and Expansion Strategies by Hyperscalers

• Global Deployment: Google and AWS are rapidly deploying custom chips across their global data centres. AWS plans to integrate Tranium2 and Inferentia2 into its AI services, including Amazon SageMaker, by mid-2025 to deliver higher efficiency and lower costs for customers running large AI models.
• Scaling AI: Nvidia’s upcoming Blackwell GPUs, expected to deliver four times the AI training performance of the H100, are being designed specifically for hyperscale environments. These GPUs will drive innovation in industries like healthcare, autonomous vehicles, and scientific research.
• Expanding at the Edge: Arm-based CPU processors like Google Axion and AmpereOne are gaining traction in edge data centres. AmpereOne, featuring up to 192 cores, provides high-density, low-power computing, making it ideal for real-time processing in IoT and smart city applications.

Frontline Chipmakers: Key Players Redefining the Market

The competition in the data centre chip market is heating up, with industry leaders like Intel and AMD driving innovation, while hyperscalers will continue to develop in-house solutions.

Intel’s Latest Releases: Powering High-Density Computing

• Xeon 6 Processors: Intel’s next-gen Xeon processors, Sierra Forest (E-cores) and Granite Rapids (P-cores), are expected to cater to hyperscalers needing scalable and energy-efficient solutions. Sierra Forest will feature up to 288 cores, offering high throughput for cloud native workloads, while Granite Rapids will focus on peak performance, particularly for AI and HPC applications.
• Gaudi 3 AI Accelerator: Promising up to 50% faster training and 40% better power efficiency than competing GPUs, Gaudi 3 is designed to support both AI training and inferencing. In late 2024, Intel’s partnership with cloud giants initiated deployment of Gaudi 3 in hyperscale data centres across Europe and North America. This is expected to accelerate in 2025.

AMD’s Competitive Edge: High-Performance Solutions

• EPYC Turin CPUs: these processors, based on the Zen 5 architecture, are expected to set new standards in performance per watt. Featuring higher core counts and advanced memory expansion capabilities, they will target hyperscale and enterprise workloads that demand high parallelism.
• Instinct MI300 GPUs: the MI300 series integrates CPU and GPU cores on a single package. The MI300X, specifically designed for generative AI and LLMs, offers 192 GB of HBM3 memory, making it a compelling option for hyperscalers running advanced AI workloads.

Hyperscalers Driving Custom Chip Innovation

• Google Axion Processors: built on the Arm Neoverse V2 architecture, Google’s Axion processors are tailored for cloud-native workloads requiring high core density and low power consumption. Google plans to deploy Axion across its compute engine and Kubernetes engine services by 2026.
• AmpereOne CPUs: Ampere’s latest offering provides up to 192 cores and is designed to handle multi-tenant cloud environments efficiently. It is already being adopted by European cloud providers for energy-efficient data centre operations.

Specialised AI Chips: Increased Workloads and Reduced Costs

AI-specific chips are becoming essential in modern data centres, enabling efficient handling of both training and inferencing workloads.

How AI Chips Are Leading the Way

• Accelerating AI Training: high-performance GPUs like Nvidia’s H100 and AMD’s MI300 dramatically reduce AI training times, enabling faster deployment of services such as virtual assistants and recommendation systems. Nvidia's Blackwell, due to be operational in 2025, is poised to exceed expectations by providing up to 20 petaflops of AI performance on a single chip.
• Streamlining AI Inferencing: inferencing chips, such as AWS’s Inferentia2 and Meta’s MTIA processors, handle repetitive AI tasks more efficiently, resulting in lower power consumption and reduced operational costs. Meta’s MTIA chips, specifically designed for AI inferencing, have already reduced the company’s inferencing costs by 30%.
• Cutting Costs: custom AI inferencing chips, such as Google Cloud’s TPU v5p and AWS’s Inferentia2, provide significant cost savings compared to traditional GPUs. AWS reports that customers using Inferentia2 can achieve up to 50% lower inference costs.

Hyperscaler Deployment Plans

• Meta’s MTIA Chips: deployed across Meta’s North American data centres, these chips will be expanded to Europe and Latin America in 2025 to support AI-driven applications such as content moderation and personalised recommendations.
• AWS’s AI Infrastructure: AWS plans to expand its AI infrastructure, powered by Tranium2 and Inferentia2, to the emerging markets of South America and Southeast Asia. These chips will support the growing demand for AI services in fintech, healthcare, and retail.
• Microsoft’s Azure Rollout: Microsoft’s Azure cloud platform is rolling out its Maia AI accelerators and Cobalt CPUs across its global regions. The Maia accelerator, designed for generative AI workloads, offers superior performance for tasks such as text generation and image synthesis.

Energy Efficiency: Innovations for Sustainable Growth

As data centres scale operations, energy efficiency becomes increasingly critical. Chipmakers are prioritising designs that minimise power consumption while maintaining top-tier performance.

Leading Energy-Efficient Chip Designs

• Nvidia Blackwell GPUs: with claims of up to 25 times better energy efficiency compared to previous generations, Blackwell GPUs are poised to significantly reduce the environmental impact of AI workloads.
• Intel Gaudi 3: designed for energy-efficient AI inferencing, Gaudi 3 accelerators promise a significant reduction in power usage, making them ideal for hyperscalers seeking sustainable growth.
• Arm-Based CPUs: Arm processors, including Google Axion and AmpereOne, are widely recognised for their low power consumption and are being deployed in hyperscale and edge data centres focused on green technology.

What Lies Ahead: Key Trends Shaping the Future of Data Centre Chips

Looking at what's coming in 2025 and beyond, several critical trends are poised to reshape the data centre chip landscape.

1. Rise of Custom Silicon: Hyperscalers will continue investing in their in-house chip designs to optimise performance and reduce costs. By tailoring processors to their unique workloads, hyperscalers like AWS and Google can enhance efficiency and lower operational expenses. AWS’s Graviton series and Google’s Axion processors exemplify how custom silicon can drive competitive advantages by reducing dependence on third-party chipmakers.
2. AI Inference at Scale: As AI models mature, efficient inferencing solutions will become increasingly vital. AI inferencing chips, such as Meta’s MTIA and AWS’s Inferentia2, will play a key role in handling the growing number of real-time AI applications, from voice recognition to predictive maintenance. Efficient inferencing at scale will allow enterprises to deploy AI-powered services more cost-effectively, with lower latency and energy consumption.
3. Quantum Advancements: Quantum computing, though still in the early stages of its much anticipated development, holds immense potential for revolutionising high-performance computing (HPC) in data centres. Companies like IBM and Google are making significant investments in quantum processors. Once scalable quantum processors become commercially viable, they could unlock new capabilities for solving complex problems in fields such as cryptography, materials science, and financial modelling.
4. Eco-Friendly Chips: The drive towards sustainability will push chipmakers to innovate further in energy-efficient architectures. Low-power designs, advanced cooling techniques, and the integration of renewable energy sources will be crucial in making future data centres more environmentally sustainable. Nvidia’s Blackwell and AMD’s MI300 series are already setting benchmarks in energy efficiency, while Intel’s Xeon processors, designed with improved performance per watt, are also expected to contribute significantly.
5. Emerging Market Expansion: Hyperscalers will prioritise expanding into regions with renewable energy potential to support sustainable data centre growth sources, which will be crucial in making future data centres more environmentally sustainable. Hyperscalers are expected to integrate these green technology solutions alongside renewable energy initiatives, further enhancing sustainability.

With these trends shaping the industry, the future of data centre chips looks promising, paving the way for a more connected, efficient, and sustainable digital world.

Conclusion: Driving the Digital Future with Next-Gen Chips

• The data centre chip industry is at a pivotal moment, driven by the need for performance, efficiency, and sustainability.
• From Nvidia’s Blackwell GPUs to Google’s Axion CPUs, these next-generation chips are not only powering AI and cloud services, but also setting new benchmarks for green technology.
• As hyperscalers and chipmakers continue to push technological boundaries, the upcoming decade is poised to transform the global digital landscape.

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