Forrester’s bottom line? “Because of the long term planning and all of the potential policy changes, I wouldn’t change my data center plans that much,” Nguyen said. Confusion reigns Every day it seems, the tariff situation becomes muddier. For example, according to a fact sheet released Wednesday, the White House has temporarily exempted semiconductors from tariffs, but not the aluminum used to build the servers and racks that house them. Furthermore, Scott Bickley, advisory fellow at the Info-Tech Research Group, said it is important to note how the various countries match with the various components. “Just about every major cost center for the buildout of a data center will be severely impacted by the new tariffs. Servers and hardware, including semiconductors, memory, network components, cabling, construction materials are going to see prices rise overnight once the tariffs go into effect,” Bickley said. “Consider that China, which has a 54% full tariff, is a major source of raw materials and rare earth elements essential for manufacturing DC components while Taiwan, at a 32% tariff rate, is the sole-source provider country for most advanced chipsets used in AI, cell phones, and any modern application footprint requiring high performance in a small footprint. South Korea (25% tariff) is a key provider of memory chips, while Japan (24%), Germany (20% EU rate), and the Netherlands (20% EU rate) are providers of sub-components like server racks, cooling systems, and semiconductor equipment.” But, he continued: “Now factor in the offshore/nearshore contract manufacturers like Mexico and Vietnam (46%) for electronics manufacturing (assembly and distribution) and Malaysia (10%) for semiconductor packaging, and it is clear to see that the complete technology supply chain leading into the data center will be taxed at multiple touchpoints.” Put all of that together and Info-Tech anticipates a lot of enterprise data center pain.

The latest release also broadens its scope beyond chatbot benchmarks. A new graph neural network (GNN) test targets datacenter-class hardware and is designed for workloads like fraud detection, recommendation engines, and knowledge graphs. It uses the RGAT model based on a graph dataset containing over 547 million nodes and 5.8 billion edges. Judging performance Analysts suggest that these benchmarks will make it easier to judge the performance of various hardware chips and clusters based on documented models. “As every chipmaker seeks to prove that its hardware is good enough to support AI, we now have a standard benchmark that shows the quality of question support, math, and coding skills associated with hardware,” said Hyoun Park, CEO and Chief Analyst at Amalgam Insights.  Chipmakers can now compete not just on traditional speeds and feeds, but in mathematical skill and informational accuracy. This benchmark provides a rare opportunity to add new performance standards on cross-vendor hardware, Park added. “The latency in terms of how quickly tokens are delivered and the time for the user to see the response is the deciding factor,” said Neil Shah, partner and co-founder at Counterpoint Research. “This is where players such as NVIDIA, AMD, and Intel have to get the software right to help developers optimize the models and bring out the best compute performance.” Benchmarking and buying decisions Independent benchmarks like those from MLCommons play a key role in helping buyers evaluate system performance, but relying on them alone may not provide the full picture.

Will it lead to shortages? The US first placed export controls on chips sent to China in October 2022 as a means to slow the country’s technological advances. It blocked the sale of Nvidia’s A100 and H100 chips, leading the company to develop the less powerful A800 and H800 chips for the market; they were also subsequently banned. There was a surge in demand for the H20 following the arrival of Chinese startup DeepSeek’s ultra low-cost, open-source AI model in January. And while the H20 is reported to be 15 times slower than Nvidia’s newest Blackwell chips sold elsewhere in the world, it was designed specifically by Nvidia to comply with the further US export controls introduced in October 2023. It is being used by Chinese companies for training, although it’s billed as an inference chip, explained Matt Kimball, VP and principal analyst for datacenter compute and storage at Moor Insights & Strategy. Should Nvidia choose to focus its efforts on manufacturing more of the chips, Kimball said he doesn’t think it will impact supply in the US and Europe, as Blackwell is the main product sold in those markets and H20 is an N-1 Hopper architecture chip. “If you take this a step further and ask whether this large order slows down the production of chips destined for the US and Europe, I’d say the answer is no, as the Hopper family is built on a different process node than the Blackwell family,” he said. Still, Kimball noted, “supply chain management is difficult, especially for smaller organizations that are put to the back of the line as hyperscalers with multibillion dollar orders are first in line for the newest [chips].”

But analysts have questioned whether the Microsoft move truly addresses those European business concerns. Phil Brunkard, executive counselor at Info-Tech Research Group UK, said, commenting on last month’s announcement of the EU Data Boundary for the Microsoft Cloud,  “Microsoft says that customer data will remain stored and processed in the EU and EFTA, but doesn’t guarantee true data sovereignty.” And European companies are now rethinking what data sovereignty means to them. They are moving beyond having it refer to where the data sits to focusing on which vendors control it, and who controls them. Responding to the new Euro cloud plan, another analyst, IDC VP Dave McCarthy, saw the effort as “signaling a growing European push for data control and independence.” “US providers could face tougher competition from EU companies that leverage this tech to offer sovereignty-friendly alternatives. Although €1 million isn’t a game-changer on its own, it’s a clear sign Europe wants to build its own cloud ecosystem—potentially at the expense of US market share,” McCarthy said. “For US providers, this could mean investing in more EU-based data centers or reconfiguring systems to ensure European customers’ data stays within the region. This isn’t just a compliance checkbox. It’s a shift that could hike operational costs and complexity, especially for companies used to running centralized setups.” Adding to the potential bad news for US hyperscalers, McCarthy said that there was little reason to believe that this trend would be limited to Europe. “If Europe pulls this off, other regions might take note and push for similar sovereignty rules. US providers could find themselves adapting to a patchwork of regulations worldwide, forcing a rethink of their global strategies,” McCarthy said. “This isn’t just a European headache, it’s a preview of what could become a broader challenge.”

The top strategy so far is what one enterprise calls the “Cloud Team.” You assemble all your people with cloud skills, and your own best software architect, and have the team examine current and proposed cloud applications, looking for a high-level approach that meets business goals. In this process, the team tries to avoid implementation specifics, focusing instead on the notion that a hybrid application has an agile cloud side and a governance-and-sovereignty data center side, and what has to be done is push functionality into the right place. The Cloud Team supporters say that an experienced application architect can deal with the cloud in abstract, without detailed knowledge of cloud tools and costs. For example, the architect can assess the value of using an event-driven versus transactional model without fixating on how either could be done. The idea is to first come up with approaches. Then, developers could work with cloud providers to map each approach to an implementation, and assess the costs, benefits, and risks. Ok, I lied about this being the top strategy—sort of, at least. It’s the only strategy that’s making much sense. The enterprises all start their cloud-reassessment journey on a different tack, but they agree it doesn’t work. The knee-jerk approach to cloud costs is to attack the implementation, not the design. What cloud features did you pick? Could you find ones that cost less? Could you perhaps shed all the special features and just host containers or VMs with no web services at all? Enterprises who try this, meaning almost all of them, report that they save less than 15% on cloud costs, a rate of savings that means roughly a five-year payback on the costs of making the application changes…if they can make them at all. Enterprises used to build all of

“Silicon photonics can transform HPC, data centers, and networking by providing greater scalability, better energy efficiency, and seamless integration with existing semiconductor manufacturing and packaging technologies,” Jagadeesan added. “Lightmatter’s recent announcement of the Passage L200 co-packaged optics and M1000 reference platform demonstrates an important step toward addressing the interconnect bandwidth and latency between accelerators in AI data centers.” The market timing appears strategic, as enterprises worldwide face increasing computational demands from AI workloads while simultaneously confronting the physical limitations of traditional semiconductor scaling. Silicon photonics offers a potential path forward as conventional approaches reach their limits. Practical applications For enterprise IT leaders, Lightmatter’s technology could impact several key areas of infrastructure planning. AI development teams could see significantly reduced training times for complex models, enabling faster iteration and deployment of AI solutions. Real-time AI applications could benefit from lower latency between processing units, improving responsiveness for time-sensitive operations. Data centers could potentially achieve higher computational density with fewer networking bottlenecks, allowing more efficient use of physical space and resources. Infrastructure costs might be optimized by more efficient utilization of expensive GPU resources, as processors spend less time waiting for data and more time computing. These benefits would be particularly valuable for financial services, healthcare, research institutions, and technology companies working with large-scale AI deployments. Organizations that rely on real-time analysis of large datasets or require rapid training and deployment of complex AI models stand to gain the most from the technology. “Silicon photonics will be a key technology for interconnects across accelerators, racks, and data center fabrics,” Jagadeesan pointed out. “Chiplets and advanced packaging will coexist and dominate intra-package communication. The key aspect is integration, that is companies who have the potential to combine photonics, chiplets, and packaging in a more efficient way will gain competitive advantage.”