Fujitsu will start selling its Monaka processors, and servers packing them, in November. The Japanese giant started talking about Monaka way back in 2023, when it teased the processor as being derived from the silicon it designed for the Fugaku supercomputer, but adapted for datacenter use. Fugaku was the world’s most powerful supercomputer from 2020 to 2022, and remains in ninth place on the Top 500 list six years after its debut. Who wouldn’t want the tech that powered that behemoth in their datacenter? Yet as The Register reported earlier this year, Monaka diverged significantly from the silicon inside Fugaku. It’s built on Broadcom’s 3D-chip stacking tech and use the Armv9 ISA. The chip features a quartet of 2nm compute dies, each packing 36 cores. Also inside the chip are four SRAM 5nm chiplets, and it’s all connected by a central I/O and memory die with 12 channels of DDR5 and PCIe 6.0 connectivity. For years, Fujitsu told the Monaka-curious that the chip would see the light of day in 2027. Earlier this week Fujitsu announced it will start selling the chips, and servers that use it, from November. The company will sell the chip to cloud operators and server manufacturers. The latter will have to compete with Fujitsu itself, which has created three servers that use the CPU: A 1U server that can pack one or two processors, running at either 2.1GHz or 2.9GHz, room for eight E3.S SSDs and two M.2 SSDs, and the ability to run in both air-cooled environments where the temperature hits 40C and cope with 45C when using liquid cooling. A 2U server with a pair of CPUs running at 2.1GHz, four E3.S disks and two M.2s, and supporting only air cooling. A 2U, dual-CPU model designed to be used for multi-node deployments, four to a rack, running at 2.9GHz, packing just two E1.S SSDs and the two M.2s, and requiring liquid cooling. This is the model Fujitsu thinks will suit HPC and academic users. All the servers use a 144-core processor. Fujitsu hasn’t named a price, at least in public. It is of course touting it as a strong option for inferencing workloads, thanks to a claimed ability to handle double the inferencing output of rival CPUs. That quality alone will gather some attention from AI giants, hyperscalers, and neoclouds, if only because silicon supply chains remain fragile and any new source of inferencing power is worthy of investigation. The fact that these machines can run in quite hot air-cooled datacenters won’t hurt. Whether any server-makers beyond those who supply hyperscalers buy in is less certain. For nigh-on a decade now, vendors of Arm CPUs have touted their low power consumption as a must-have, but manufacturers have made only token efforts to deliver products that host Arm chips. Interestingly, Fujitsu is pitching its smaller servers as a sovereign solution, in part because it’s making the machines in Japan. Europe, the other market into which Fujitsu will initially sell the boxes, is also increasingly interested in sovereign tech. ®
Nvidia’s ability to sell GPUs is ultimately limited by how much juice the power grid can provide. With ever-growing depreciation cycles, it’ll be years before datacenters decommission their aging Hopper or Blackwell systems. More GPUs mean pulling more power from the grid. Nvidia can’t exactly force grid operators to add capacity any faster, but it can make it easier for its customers to build smarter and more efficient bit barns. “At the datacenter scale and at the AI-factory scale, we're literally trying to think about how can we eke out every bit of efficiency to drive more performance per gigawatt,” Dion Harris, senior director of Nvidia HPC and AI Hyperscale Infrastructure Solutions, told El Reg in a recent interview. At the AI Infra Summit this week, we got our first look at the systems Nvidia has been building to maximize the amount of power available for compute while minimizing the impact of datacenters on the local grid. Datacenters, as a general rule, rarely operate anywhere close to the peak capacity. A 100 megawatt datacenter might use at most 80 percent for critical compute loads. The actual ratios vary from bit-barn to bit-barn, but this provides a buffer for hardware inefficiency, conversion losses, and other spikes in demand. The downside, of course, is that this leaves 20 megawatts or so of untapped capacity that the datacenter can’t use and the utility can’t reclaim. Every kilowatt of stranded power is a GPU that Nvidia could have sold, so Nvidia’s DSX platform aims to address both problems. Minimizing overheads The first of these, which Nvidia calls DSX MaxLPS, is an evolution of an old idea. If the compute and all the physical infrastructure — power cabinets, batteries, coolant distribution units (CDUs), and chillers — can talk to one another, operators can achieve significant power savings. For example, if the air handlers had a way of knowing how much power a rack was pulling, they could ramp up and down based on demand rather than running maxed out all the time. The challenge, as you might expect, is getting systems from dozens of different vendors to speak the same language. So while the idea sounds great in theory, getting everyone on the same page was easier said than done. That changed with the widespread deployment of AI systems, Harris explained. More efficient bit barns can churn out more tokens, which translate into higher revenues — so long as people are willing to pay for the tokens anyway. However, Nvidia still needed to address the communications layer, something that was no doubt made easier by the fact its GPUs are the hottest commodity in the world right now. “DSX Exchange is really kind of an API that allows us to capture information not just around the core systems,” Harris explained. “We can capture information from the other DSX ready providers that provide assets. That would be like Vertiv and Schneider Electric, and all the building management systems." At the AI Infra Summit this week, Nvidia and Lambda showed how this bridge, working as part of its broader DSX MaxLPS offering, could be used to pack more compute into the same power envelope. In testing, Lambda was able to cram 19 nodes into the same power budget normally occupied by 16, boosting cluster-wide throughput by 24 percent and performance per watt by a similar margin. The result, the company claims, is that datacenters don’t need to overprovision their bit barns to the same extent since they have greater visibility and control over the datacenter as a whole. Shedding the load Along with helping its customers get more out of their bit barns, Nvidia also showed how its datacenter management tech could be used to minimize the impact of large AI training and inference deployments on the local grid. Working with Emerald AI and Silicon Valley Power, Nvidia demonstrated how its DSX Flex platform could be used to free up datacenter power when grid demand spikes without disrupting critical workloads. But just like Nvidia’s DSX MaxLPS offering, the underlying tech isn’t exactly new. Demand response has been around for years now and allows utilities to ask power hungry industries to curb their energy use during periods of peak demand. Google and others have been toying with this tech for some time now. You may recall last year when it announced it would pause non-essential AI workloads in order to avoid overloading the grid. Nvidia’s DSX Flex aims to bring this capability to anyone deploying its hardware. But this tech may be less about keeping AI from causing brownouts and more about getting utilities to green light additional capacity on the proviso that they can reclaim some portion of it at a moment's notice. “It allows, in some cases, the grid providers, transmission line owners, to be a little a little bit less conservative in how they allocate or over provision because now, knowing that you have the ability to curtail within a certain window, they don't have to have as much headroom,” Harris explained. But just because a utility claws back capacity doesn’t necessarily mean that the workloads shut down, he notes. While critical workloads keep running, deep integration with Nvidia's software stack means that non-essential workloads can either be paused or migrated to neighboring datacenters with excess capacity — a concept sometimes described as chasing the sun. Another walled garden As you probably already guessed, while Nvidia’s DSX platform works great for AI factories built using validated hardware, things get a bit more complicated as third-party accelerators from the likes of d-Matrix, SambaNova, and others are added to the mix. For partners, like d-Matrix already living inside its NVLink Fusion ecosystem, Harris sees a path forward for extending support for DSX to these platforms, though he notes that some degree of software integration will be required and not every chip will expose the same level of granular control as its own chips do. When it comes to competing platforms, like AMD’s Instinct GPUs, bit barn builders will likely need to look to alternative datacenter management systems to replicate DSX’s capabilities. So, on top of making the most of the limited grid capacity available today, Nvidia’s DSX is another walled garden that ensures its customers continue buying its equipment. ®
Companies are getting more selective about replacing legacy kit such as mainframe systems, and rising AI investment is one of the factors causing them to hold onto these existing assets for longer. Managed services biz Ensono claims in its 2026 State of IT Modernization report that 78 percent of IT decision makers regard legacy systems as more important today than they did two years ago, and this is because such systems are viewed as key to making AI work for their organization. The report is based on a survey of 500 IT decision-makers and line-of-business leaders conducted by the firm across the US and UK. Its findings show that AI is reshaping modernization priorities, but that corporates are still grappling with many of the challenges that have traditionally complicated modernization efforts, such as budget overruns, delayed initiatives, and talent shortages. Ensono found that 45 percent of firms are actively scaling AI deployments across their organization, while 44 percent are investing in targeted, high-impact use cases. Supporting AI, automation, and advanced data initiatives is now the top pressure driving modernization, while more than half of companies say that AI is helping advance modernization by delivering through greater automation and improved efficiency. Among the barriers businesses face, the top challenges to achieving AI goals are said to be difficulty integrating AI into existing workflows and business processes (33 percent of respondents) and infrastructure limitations (28 percent). For this reason, Ensono says that those legacy systems are taking on new strategic value as organizations attempt to scale their AI rollout. Nearly half of organizations view legacy systems such as mainframes as a critical foundation for AI and an important source of data for this purpose, while 47 percent use them selectively for specific AI applications. More than half of organizations are optimizing and extending legacy systems while modernizing applications where they are, rather than replacing them outright. This is an approach that’s slightly more common in the UK (57 percent of respondents) than the US (48 percent). “Enterprises are discovering that legacy systems, like the mainframe, are intensely powerful, reliable and efficient sources of computing that can now be augmented and made more agile thanks to AI,” says Ensono’s Chief Strategy Officer Brian Klingbeil. “These systems contain decades of data and business logic that drive many companies. The advantage will go to organizations that understand their environments well enough to distinguish what should be replaced versus what should be augmented, and embrace new modernization methods that simply were not available even 18 months ago.” However, using the mainframe as a foundation for AI rollouts was noted by infrastructure services firm Kyndryl two years ago. It found that big iron was becoming a prime candidate to host and run AI workloads, while enterprises were increasingly integrating their mainframes with modern infrastructure. This meant moving some workloads off the mainframe while updating others in place to continue to benefit from the security and reliability of the platform. Analyst Gartner also said earlier this year that migrating workloads to a mainframe made more sense for VMware users than adopting Broadcom’s new licenses. Elsewhere, HPE managing director for UK, Middle East and Africa said that enterprise customers are opting to sweat IT assets for longer, moving refresh cycles from five years to seven years, for example. “I think we're seeing earlier planning for critical projects. I think a lot of customers are looking much further ahead on their roadmap of spend at the things they have to do, because maybe some technology is going end of support or the lease is expiring at the datacenter,” he said. “So I think you're seeing a decision tree of, do we pause here, and do we sweat the asset for a little bit longer, or is this essential to move ahead? I think it's fundamentally forcing a rethink for customers.” ®
Login
