Latest news with #GPUclusters
Reuters
22-07-2025
- Business
- Reuters
What Happens When Chips Outpace the Networks That Feed Them?
SAN FRANCISCO, CA, July 22, 2025 (EZ Newswire) -- In the past few years, semiconductor technology has advanced rapidly — so much that Nvidia recently became the world's first $4 trillion company, opens new tab, thanks to the huge demand for AI chips. Today's top GPUs and AI accelerators can process massive amounts of data incredibly fast. But this brings up an important question: Can our networks keep up and deliver data quickly enough to keep these powerful chips working at full speed? In many cases, the answer is no. A growing performance gap means high-end processors often sit idle, starving for data, while relatively sluggish networks struggle to keep up. Bridging this gap will require new strategies — from intelligent proxy servers to massive infrastructure upgrades — to ensure the AI revolution isn't bottlenecked by bandwidth and latency. , opens new tabProxy Servers Step In to Balance Load and Manage AI-Era Latency When real-time AI applications demand instant responses, clever use of proxy server, opens new tab platforms and gateways has become essential. Unlike a basic intermediary, modern AI proxies do far more than pass along traffic. They perform smart routing, load balancing, and caching to optimize data flow between users, devices, GPU clusters, and cloud APIs. For instance, an AI gateway can direct your request to the least busy or closest server, manage timeouts and retries, and monitor performance — all in the blink of an eye. By pushing computation to the edge of the network, self-driving cars can avoid round-trip delays to distant data centers — the goal is to eliminate every unnecessary nanosecond from vehicle-to-everything (V2X) communication. Services like Webshare, opens new tab make this architecture easier to deploy by offering scalable proxy infrastructure with configurable bandwidth, rotation, and geolocation. Instead of creating a proxy system from the ground up, teams can quickly tap into a large pool of IP addresses designed for tasks like AI inference, data collection, or edge delivery. Let's talk about another aspect. Semantic caching of LLM (large language model) responses can transform response times from seconds to milliseconds. In other words, answers that once took a few seconds on a busy model could be delivered near-instantly from a proxy's cache. Similarly, content delivery networks (CDNs) function as proxy layers across the globe, bringing data physically closer to users to speed up streaming and video AI processing. And when fresh computation is needed, proxies help balance the load. They distribute incoming requests across fleets of GPUs so no single server gets swamped, preventing slowdowns. , opens new tabCompute Power Meets Network Limits: A Growing Bottleneck Despite these optimizations, the broader problem remains — today's chips are outrunning the networks that feed them. We see it clearly in advanced AI training, which often spreads one job across hundreds of GPUs in parallel. Those GPUs need to swap results continuously over the network to synchronize with each other. If the interconnect is too slow, the GPUs end up idle, twiddling their thumbs as they wait for data. 'Job completion time is determined by how quickly GPUs can turn out results and how quickly the network can synchronize those results,' the source explains, opens new tab. Improving network throughput and latency can thus unlock hidden performance. In fact, even small upgrades to network infrastructure can 'bring up [GPU utilization] rate' and yield 'millions of dollars in savings' by avoiding wasted idle time. Looking further ahead, entirely new network paradigms are emerging to keep pace with Moore's Law. One promising route is optical interconnects. Today's server racks still rely on copper wires, but electrical signaling is nearing its limits for high bandwidth over distance. Companies like Ayar Labs are pioneering in-package photonics to beam data as light. Their optical chiplets can blast terabits of data per second between chips with dramatically lower latency and power draw than copper traces. As the professionals put it, opens new tab, the conventional electrical architecture is 'rapidly approaching the end of its roadmap,' and future chip-to-chip links will require photonics. By converting electronic signals to light right at the source, these optical networks could prevent tomorrow's ultrafast CPUs and AI accelerators from being starved for data. In summary, a multi-pronged effort — faster switch silicon, smarter network cards, and even lasers in our chips — is underway to close the gap between what our chips can chew through and what our networks can supply. As chips get faster and more powerful, our networks are struggling to keep up. But progress is being made. New technologies, as we see these days, are helping close the gap between computing and data delivery. The fact that companies put all the effort into improving not only the hardware which are chips but also the way data is transferred, talks about their dedication to avoid slowdowns and make sure AI reaches its full potential. That means delivering real-time, smart performance everywhere — from data centers to self-driving cars. In the end, success will go to those who build not just the fastest processors, but also the fastest systems to connect them. Media Contact Joris Leitonasjoris@ ### SOURCE: Webshare Copyright 2025 EZ Newswire See release on EZ Newswire
Fast Company
17-07-2025
- Business
- Fast Company
Meta is using tents to build its giant AI data centers
When Mark Zuckerberg announced on July 14 that his company Meta was embarking on a project to build massively power-hungry data centers to support its ambitions for advancing artificial intelligence, the imagery that accompanied his posts on Facebook and Threads was stark. The data centers he was announcing would have power requirements upwards of five gigawatts and, to show just how big that would be, Zuckerberg's post included a visual of a gigantic rectilinear block covering a sizable portion of Manhattan. It was as if the city were suddenly snuffed out by millions of square feet of artificial intelligence infrastructure. A detail that was not included in Zuckerberg's initial announcement was the curious way these massive data centers are currently being built. In an interview with the Information, Zuckerberg briefly explained that part of the way Meta is building out its multi-gigawatt data centers is by using quickly constructed hurricane-proof tents. 'We have a very strong infrastructure team that is doing novel work to build up data centers,' Zuckerberg said. 'I wanted them to not just take four years to build these concrete buildings, so we pioneered this new method where we're basically building these weatherproof tents and building up the networks and the GPU clusters inside them in order to build them faster.' A Meta spokesperson confirmed to Fast Company that tents are currently being set up as part of at least one of the multi-gigawatt data centers the company is building, located in New Albany, Ohio. Dubbed 'rapid deployment structures,' the tents are long rectangular buildings made of puncture- and water-proof fabric supported by an aluminum substructure with a mushroom-esque pitched roof. The Ohio data center, which Meta has named Prometheus, is an already existing complex that is having additional computing capacity added through these server tents. Meta expects the facility to be big enough to draw more than one gigawatt of power by 2026. It is one of the world's largest AI training clusters, according to the AI and semiconductor research company SemiAnalysis. The rapidly built tent structures there are part of the way Meta aims to meet its gigawatt goal next year. Tents may be part of another multi-gigawatt data center Meta is building in Richland Parish, Louisiana. Named Hyperion, it's anticipated to pull two gigawatts of power by 2030, with the potential to grow to five gigawatts. Meta's spokesperson says construction has been underway in Louisiana for months. The data center being built there will encompass 11 buildings adding up to more than four million square feet. The site covers roughly three square miles, so there's plenty of space to expand. But even at capacity, it's far less than the 22 square miles of land that makes up Manhattan. How much of this space will be initially made up of tents remains to be seen. But as the arms race and talent competition heat up between AI-focused companies like Meta, OpenAI, Alphabet, and Microsoft, the size of these tents may be less important than how quickly they can be constructed.
