Shaping the Future of AI Data Centers via Connectivity and Cabling

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Shaping the Future of AI Data Centers via Connectivity and Cabling

At Supercomputing 2024 (SC24) in Atlanta, two announcements might have set a new course for the future of AI data centers. Enfabrica Corporation, a pioneer in high-performance networking silicon, revealed the general availability of its 3.2 Terabit/sec (Tbps) Accelerated Compute Fabric (ACF) SuperNIC chip, which promises advancements in GPU networking capabilities. Meanwhile, Point2 Technology, known […]

At Supercomputing 2024 (SC24) in Atlanta, two announcements might have set a new course for the future of AI data centers.

Enfabrica Corporation, a pioneer in high-performance networking silicon, revealed the general availability of its 3.2 Terabit/sec (Tbps) Accelerated Compute Fabric (ACF) SuperNIC chip, which promises advancements in GPU networking capabilities.

Meanwhile, Point2 Technology, known for its energy-efficient interconnect solutions, introduced its P1B121 UltraWire Smart Retimer SoC, designed to address the increasing energy demands of AI-driven hyperscale data centers.

With AI, Connectivity Is Crucial

With AI supercomputers typically being composed of thousands, sometimes millions, of interconnected processing units any technologies that impact those interconnections can have a significant impact.

High-speed, low-latency internal networks, such as InfiniBand or high-performance Ethernet (currently 400/800 Gbe), are critical to facilitating this communication. Without such connectivity, the synchronization of tasks across processing units suffers, resulting in inefficiencies and reduced computational performance.

In AI research and development, many supercomputers are part of global collaborations. Shared datasets, federated AI model training, and distributed simulations necessitate secure and ultra-reliable network connectivity across international boundaries.

Any interruptions or inefficiencies in these connections can disrupt the research process, slowing down potential breakthroughs due to connectivity issues.

Speed, Speed, and More Speed

Enfabrica’s 3.2 Tbps ACF SuperNIC looks to accelerate AI data center technology, delivering four times the bandwidth compared to the current standard of GPU-attached network interface card.

Built from the ground up to cater to the needs of large-scale AI models, such as training, inference, and retrieval-augmented generation (RAG) workloads. Enfabrica identifies 3 key features that make their NIC a strong contender as an AI connectivity choice:

Bandwidth and scalability
- Each ACF-S chip includes 32 network ports and 160 PCIe lanes, enabling GPU clusters of over 500,000 units.
- The solution supports 800, 400, and 100 Gigabit Ethernet interfaces, offering unprecedented scale-out throughput.
Software defined networking adds resiliency and flexibility
- With Resilient Message Multipathing (RMM) technology, the minimizes job stalls due to network failures, boosting GPU utilization without altering existing AI software stacks.
- The chip’s Software-Defined RDMA Networking enhances flexibility, allowing operators to customize transport layers for optimized cloud-scale networks.
Simplifies operation
- By integrating zero-copy data transfers and optimized memory management, to enhance floating-point operations per second (FLOPs) utilization, critical for large-scale AI clusters.

Enfabrica CEO Rochan Sankar described the launch as a pivotal moment:

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Quantum networking advances on Earth and in space

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What is Transport Layer Security? How TLS1.3 protects your data

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Who wins in networking in 2025 and beyond?

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SSE on track despite stormy weather

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TriMas Completes Arrow Engine Sale

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Tackling mental health as a young professional

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Scotland’s Mocean Energy targets Australia and US for expansion

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NGOs win critical seabed minerals skirmish with Norwegian government

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Aberdeen’s Well-Safe solutions secures contract extension with Eni

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Linux containers in 2025 and beyond

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Let’s Go Build Some Data Centers: PowerHouse Drives Hyperscale and AI Infrastructure Across North America

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Blue Owl Swoops In As Major Backer of New, High-Profile, Sustainable U.S. Data Center Construction

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Global Data Center Operator Telehouse Launches Liquid Cooling Lab in the UK to Meet Ongoing AI and HPC Demand

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Why DeepSeek Is Great for AI and HPC and Maybe No Big Deal for Data Centers

In the rapid and ever-evolving landscape of artificial intelligence (AI) and high-performance computing (HPC), the emergence of DeepSeek’s R1 model has sent ripples across industries. DeepSeek has been the data center industry’s topic of the week, for sure. The Chinese AI app surged to the top of US app store leaderboards last weekend, sparking a global selloff in technology shares Monday morning. But while some analysts predict a transformative impact within the industry, a closer examination suggests that, for data centers at large, the furor over DeepSeek might ultimately be much ado about nothing. DeepSeek’s Breakthrough in AI and HPC DeepSeek, a Chinese AI startup, this month unveiled its R1 model, claiming performance on par with, or even surpassing, leading models like OpenAI’s ChatGPT-4 and Anthropic’s Claude-3.5-Sonnet. Remarkably, DeepSeek developed this model at a fraction of the cost typically associated with such advancements, utilizing a cluster of 256 server nodes equipped with 2,048 GPUs. This efficiency has been attributed to innovative techniques and optimized resource utilization. AI researchers have been abuzz about the performance of the DeepSeek chatbot that produces results similar to ChatGPT, but is based on open-source models and reportedly trained on older GPU chips. Some researchers are skeptical of claims about DeepSeek’s development costs and means, but its performance appears to challenge common assumptions about the computing cost of developing AI applications. This efficiency has been attributed to innovative techniques and optimized resource utilization. Market Reactions and Data Center Implications The announcement of DeepSeek’s R1 model led to significant market reactions, with notable declines in tech stocks, including a substantial drop in Nvidia’s valuation. This downturn was driven by concerns that more efficient AI models could reduce the demand for high-end hardware and, by extension, the expansive data centers that house them. For now, investors are re-assessing the

Microsoft will invest $80B in AI data centers in fiscal 2025

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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