The rapid evolution of artificial intelligence (AI) has spurred significant advancements in server chip technology , with several companies introducing new processors tailored for AI data centers. Here are some of the latest developments:
In March 2024, Nvidia unveiled its Blackwell architecture, introducing the B100 and B200 data center accelerators. These processors are designed to handle the demands of generative AI, offering substantial improvements in performance and efficiency over previous generations. The Blackwell architecture introduces fifth-generation Tensor Cores, supporting data types like FP4 and FP6, which enhance throughput for AI model inference.
In October 2024, AMD launched its fifth-generation EPYC server processors, built on the Zen 5 core architecture. These CPUs feature up to 192 cores and are optimized for cloud, enterprise, and AI workloads. They can function as standalone chips for general-purpose tasks or pair with AI accelerators, such as AMD's Instinct Series GPUs, to handle larger AI models and applications.
In November 2024, Microsoft introduced two custom infrastructure chips to enhance AI operations and data security within its data centers. The Azure Integrated HSM focuses on safeguarding encryption data, while the Data Processing Unit (DPU) consolidates multiple server components to optimize cloud storage tasks efficiently. These developments aim to reduce reliance on traditional processors and improve performance and cost efficiency.
In April 2024, Google announced the development of its custom Arm-based CPU, Axion, designed to support AI workloads in its data centers. This initiative reflects a broader trend of tech giants creating bespoke processors to meet specific performance and efficiency requirements for AI applications.
Cerebras Systems continues to innovate with its Wafer-Scale Engine (WSE), a massive chip designed specifically for AI workloads. The third-generation WSE-3, introduced in March 2024, boasts 4 trillion transistors and 900,000 AI-optimized cores, significantly reducing model training times and handling large AI models more effectively.
These advancements highlight the industry's commitment to developing specialized chips that cater to the growing demands of AI data centers, focusing on performance, scalability, and energy efficiency.
The server chip landscape is poised for significant advancements in 2025, with several key developments:
Intel is set to release its Granite Rapids-SP and Granite Rapids-AP processors in 2025. The Granite Rapids-SP targets mainstream servers, featuring up to 86 cores and supporting 8-channel DDR5 memory. The Granite Rapids-AP is designed for advanced performance, offering up to 128 cores, 96 PCIe 5.0 lanes, and 12-channel DDR5 memory support, with TDPs up to 500W.
Chinese startup SpacemiT announced the development of the VitalStone V100, a server processor with up to 64 RISC-V cores, manufactured using 12nm process technology. The V100 supports virtualization and is designed for next-generation AI applications, marking a significant step in RISC-V adoption for data centers.
Amazon Web Services (AWS) unveiled Trainium3, its next AI training chip, claiming four times the performance of its predecessor, Trainium2. AWS also announced Project Rainier, a supercomputer built with Trainium2 chips, aiming to be the world's largest AI compute cluster.
AMD plans to release the MI350 chip in the second half of 2025, targeting AI workloads with enhanced performance. This follows the MI325X, which is set to launch in late 2024, aiming to compete with Nvidia's H200 AI chips.
These developments indicate a competitive and innovative year ahead in server chip technology, with a focus on higher core counts, improved performance, and specialized solutions for AI and data center applications.
High-powered servers are designed to handle resource-intensive workloads, such as AI, machine learning, high-performance computing (HPC), and big data analytics. They offer higher processing power, more memory, faster storage, and advanced networking capabilities compared to standard servers. These servers are essential for data centers supporting high-density deployments and next-generation applications.
When choosing high-powered servers, consider:
The latest processors for data centers include:
Next-generation chips improve performance through:
High-powered servers are ideal for:
GPUs (Graphics Processing Units) are critical for accelerating parallel processing tasks, such as AI training and scientific simulations. Modern servers often include NVIDIA A100 or AMD Instinct GPUs for enhanced performance in AI and HPC workloads. GPUs significantly reduce processing time for large datasets and complex computations compared to CPUs alone.
High-powered servers consume significantly more power than traditional servers, often ranging from 5 kW to 30 kW per rack. Efficient cooling solutions such as liquid cooling or rear-door heat exchangers are essential to manage the heat generated. Planning for power redundancy (N+1 or 2N configurations) is also critical to ensure uptime and reliability.
The best storage solutions for high-powered servers include: