Meta, the social media giant formerly known as Facebook, has unveiled its ambitious plans to revolutionize the field of artificial intelligence (AI). At the recent AI Infra @Scale event, Meta showcased a range of groundbreaking hardware and software projects aimed at powering the next generation of AI applications. These initiatives include the development of custom silicon chips for AI models, an AI-optimized data center design, and the expansion of their massive GPU supercomputer. By reimagining its infrastructure, Meta is creating a scalable foundation to support emerging opportunities in generative AI and the metaverse.
Training and Inference Accelerator for greater efficiency
Meta has taken a significant leap forward in its pursuit of AI dominance with the development of its proprietary Meta Training and Inference Accelerator (MTIA) chip family. This in-house accelerator chip has been meticulously crafted to cater specifically to the demands of inference workloads. MTIA has superior computational power and efficiency when compared to traditional CPUs, making it an ideal choice for Meta’s internal operations.
By harnessing the combined might of MTIA chips and graphics processing units (GPUs), Meta aims to unlock a multitude of benefits across its AI infrastructure. The inclusion of MTIA chips alongside GPUs promises to deliver unparalleled performance, significantly reducing latency, and maximizing efficiency for every workload Meta handles.
The integration of MTIA chips into Meta’s AI infrastructure signifies a profound shift toward enhanced performance and streamlined operations. By strategically deploying these custom accelerators alongside GPUs, Meta empowers itself to deliver an exceptional user experience with lightning-fast response times, while also conserving energy and computational resources.
AI-focused next-gen data center
Meta is revolutionizing its data center infrastructure to support the ever-evolving landscape of artificial intelligence. The forthcoming next-generation data center design is poised to bolster Meta’s existing product lineup while laying the groundwork for future generations of AI hardware, catering to both training and inference tasks.
The centerpiece of this visionary data center design is its AI-optimized architecture, purposefully tailored to meet the unique demands of AI workloads. Liquid-cooled AI hardware will be seamlessly integrated, ensuring optimal performance and enhanced efficiency. This cooling solution not only safeguards the integrity of the AI hardware but also contributes to significant energy savings.
An integral aspect of the new data center design is the establishment of a high-performance AI network, effectively interconnecting thousands of AI chips. This interconnected network forms the backbone of data center-scale AI training clusters, facilitating massive parallel processing and enabling Meta to achieve unparalleled computational prowess. This network architecture ensures seamless communication and collaboration between AI chips, unlocking the full potential of Meta’s AI infrastructure.
The new data center design synergizes with Meta’s first in-house-developed ASIC (Application-Specific Integrated Circuit) solution, MSVP. This purpose-built ASIC caters specifically to the surging demands of Meta’s expanding video workloads. MSVP empowers Meta to handle the ever-increasing volume of video content while maintaining optimal performance and reliability.
Purpose-built AI infrastructure
The Research SuperCluster (RSC) AI Supercomputer, developed by Meta, is among the most advanced AI supercomputers globally. Its primary purpose is to facilitate the training of cutting-edge AI models that will drive the development of innovative augmented reality tools, content understanding systems, real-time translation technology, and various other applications. With 16,000 GPUs, the RSC offers unparalleled computational power. Moreover, it leverages a highly efficient 3-level Clos network fabric, enabling seamless access to all GPUs across the 2,000 training systems while maintaining maximum bandwidth capacity.
Several major players in the technology industry are actively pursuing purpose-built AI infrastructure, indicating the growing interest in this field. Microsoft and Nvidia joined forces in November to introduce an AI supercomputer in the cloud, featuring Nvidia GPUs and Nvidia’s Quantum 2 InfiniBand networking technology.
IBM followed suit in February by unveiling its own AI supercomputer, codenamed Vela. Google also entered the AI supercomputer race with an announcement on May 10. Their system employs Nvidia GPUs and custom-designed infrastructure processing units (IPUs), enabling rapid data flow and processing.
“Over the next decade, we’ll see increased specialization and customization in chip design, purpose-built and workload-specific AI infrastructure, new systems and tooling for deployment at scale, and improved efficiency in product and design support,” said Santosh Janardhan, VP & Head of Infrastructure at Meta.
Meta’s efforts in building a custom silicon chip, an AI-optimized data center design, and a powerful AI supercomputer are all part of their overarching goal to develop increasingly sophisticated AI models. Through these innovative projects, Meta aims to provide people worldwide with access to this cutting-edge technology. By integrating state-of-the-art AI models into its products, Meta seeks to enhance the user experience and empower individuals with the capabilities afforded by this emerging technology.
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