In the fast-paced world of artificial intelligence, hyperscalers like Google, Microsoft, and Amazon are pushing the boundaries of innovation with cutting-edge large language models and vast GPU clusters. But while these giants grab the spotlight, enterprises are also embarking on their own AI journeys, facing the complex challenge of optimizing their infrastructure for performance and efficiency. This growing demand for AI is reshaping the technology landscape, requiring significant changes in how we build, manage, and scale AI environments.
From the unquenchable thirst for data to the rising concerns over power consumption, the evolution of AI infrastructure is anything but straightforward.
The Rising Demand for Data Throughput
As AI models balloon in size and complexity, their hunger for data grows exponentially. These data-intensive workloads create a pressing need for high throughput infrastructure capable of moving vast amounts of information between GPUs, storage, and other components at lightning speed. Whether it’s training a new AI model or running inference tasks, every millisecond counts.
Currently, top tier hyperscaler data centers operate at 400G, but the push towards 800G is expected to come in the next year. This massive leap highlights the widening gap between hyperscalers and traditional front-end data centers, as the former rapidly adapts to the high throughput demands of AI.
Ethernet’s Rise to the Top: From Infiniband to RoCE
Historically, Infiniband has been the go-to interconnect technology for high-performance computing, especially for AI workloads. But a major shift is underway. Industry leaders are increasingly favoring Ethernet-based solutions, driven by innovations like RDMA over Converged Ethernet (RoCE). With advancements pioneered by the Ultra Ethernet Consortium, Ethernet is evolving to offer lossless data transfer and better determinism—two features critical for AI’s heavy data needs.
Hyperscalers are spearheading this transition, recognizing Ethernet’s advantages in cost, ubiquity, and scalability. With these benefits, the once-niche technology of Infiniband is being replaced by the more flexible and widely adopted Ethernet.
The Power-Hungry Nature of AI Workloads
AI’s computational demands come at a cost—power consumption. For example, a single server equipped with eight Nvidia H200 GPUs can consume over 8 kilowatts of power, creating enormous heat and requiring advanced cooling solutions. While air-cooled GPUs like Nvidia’s H100 and H200 remain popular, liquid cooling is becoming an effective way to manage rising thermal challenges.
As GPU technology continues to evolve and push the limits of processing power, organizations will need to adapt their AI infrastructure, most likely making liquid cooling the norm rather than the exception.
Data Locality: Keeping Information Close
For AI to function at its peak, data must reside close to the processing units. Powerful, expensive GPUs are only as effective as the data they can access quickly. This reality is pushing organizations to rethink their data lake strategies, ensuring that data is stored as close to the processing infrastructure as possible, limiting latency and keeping the GPUs busy. This shift is critical for maximizing AI workload performance.
The Enterprise AI Journey: A Unique Challenge
While hyperscalers have the resources and expertise to build state-of-the-art AI systems, enterprises face unique hurdles. Many companies are still in the early stages of AI adoption, struggling to identify the appropriate first use cases, choose appropriate models, and manage the complexity of MLOps (Machine Learning Operations). Enterprises need more than just infrastructure—they need guidance and support to navigate the challenges of AI adoption.
Enter Cisco HyperFabric: Simplifying AI Infrastructure
Cisco’s HyperFabric is designed to meet the demands of both hyperscalers and enterprises by offering a cloud-managed, plug-and-play solution for on-premises data center fabrics. It streamlines the process of building and managing AI clusters, making AI infrastructure more accessible to organizations of all sizes.
HyperFabric combines:
- Cisco 6000 series switches for high-performance 400G and 800G Ethernet connectivity, providing the backbone of the AI fabric.
- Cisco UCS servers with Nvidia GPUs and BlueField-3 DPUs for AI acceleration, enhanced networking, and security.
- VAST data storage to ensure that data is easily accessible to AI workloads through a high-performance storage system.
Cisco as a Trusted AI Advisor
Cisco’s expertise goes beyond infrastructure. As a trusted partner in networking, compute, and storage, the company helps enterprises navigate their AI journeys with comprehensive support. Whether it’s helping identify suitable AI use cases, selecting the right models, or implementing robust MLOps practices, Cisco provides valuable guidance at every step.
Cisco’s offerings include:
- Cisco Validated Designs (CVDs): These detailed blueprints provide best practices for deploying AI solutions, covering different modalities like text-to-text and text-to-image, as well as various infrastructure options.
- AI Advisory Services: Cisco helps organizations identify appropriate AI models, develop strategies, and find use cases that drive real value.
- Partnerships: Cisco collaborates with leading technology providers like Nvidia, Red Hat, VAST, NetApp, and Pure Storage to deliver integrated, AI-optimized solutions.
- AI-Optimized Networking: Cisco’s Nexus switches and Nexus Dashboard offer features like priority-based flow control, enhanced congestion management, and RoCE support to ensure AI workloads run smoothly.
- Automation Tools: Cisco simplifies AI infrastructure management with automation scripts and tools, reducing manual tasks and enabling faster deployment.
Why This Matters
The future of AI depends on robust, flexible, and high-performing infrastructure. By addressing key challenges like data throughput, network connectivity, power consumption, and management complexity, Cisco empowers enterprises to unlock AI’s full potential. Cisco’s comprehensive approach, including solutions like HyperFabric and a broader portfolio of AI-optimized technologies and services, positions the company as a critical partner for organizations embracing AI.
With Cisco’s expertise, enterprises can confidently navigate the evolving landscape of AI infrastructure, driving innovation and growth in a rapidly changing world.
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