AI has become a juggernaut, driving technical strategy in the tech sector and outside of it. With the worldwide server market growing 30 percent in a single year to hit $122.6bn in the first quarter of 2025, IDC now feels safe recategorizing AI-driven investment "from cyclical to durable".
The analyst company expects inference to account for over 70 percent of computing demand by next year, adding that it's growing fastest at the edge.
All this investment in AI infrastructure might once have come mainly for hyperscalers, but that's changing quickly. Adoption is spreading quickly beyond public cloud service providers into enterprise environments, IDC adds. They're eager to run agentic AI in-house.
There are plenty of reasons for companies to own their own AI infrastructure rather than offloading it to the cloud. One of these is data control, especially in regulated sectors such as finance, healthcare, manufacturing, and the public sector that have sovereign data requirements. Another revolves around the need for predictable costing from tokens delivered on tunable in-house systems.
So what should companies look for in a supplier? Delivery speed will be a powerful driver for many who are under the gun to deliver AI initiatives for the business, especially during periods of constrained memory and CPU supply. Life cycle services will also come near the top of the list, for companies that need help managing everything from the power through to the reasoning LLM models and the workloads that use them. That need for expertise is unlikely to stop; companies will undergo changes in AI strategy as they get to grips with a still-evolving technology.
ASUS expects this AI surge to continue in the datacenter, but elsewhere too, specifically in edge locations (especially industrial use cases) and at developers' desks, as the applications that will power the enterprise get made. It wants to be top of peoples' list for hardware and services at all these places along the AI continuum.
At NVIDIA's GTC show in Berlin on Oct 21-22, it announced a full-stack AI offering that it's calling its Enterprise Agentic AI Solution. It's three-layer affair, featuring:
ASUS AI Server and Infrastructure: Its selection of AI servers, including several new units.
ASUS AI Software Stack: Its software for integrating and controlling AI workflows.
ASUS AI HUB: A services operation that ties it all together.
These bring together some existing assets that ASUS had for enterprise AI users, along with some new ones. The time is right for an end-to-end offering with new hardware because of the way the market is shifting, say ASUS execs.
"ASUS servers help enterprises build high-performance agentic AI infrastructure capable of supporting both complex task execution and large-scale AI agent deployment, accelerating enterprise adoption of agentic AI," says Paul Ju, senior vice president and head of infrastructure solution BG at ASUS.
While generative AI initially spiked demand, the advent of agentic AI has sent compute demand into overdrive. Ju explains that the focus is expanding from the GPU.
"Historically, AI servers have been optimized primarily around GPU compute. But as enterprise AI shifts toward agentic AI, workloads increasingly revolve around a continuous ‘agent loop’ that is heavily CPU-dependent," he says. That loop takes in task planning, code execution, external tool invocation, and reflection or correction work.
The CPU's increased role in this execution work is creating bottlenecks in conventional AI servers, he adds.
The first such sticking point is single-thread performance, which leaves GPUs idling while they wait for CPUs to complete. The second lies in memory bandwidth. When this is managed on a per-core basis, it can slow things down when many agents run at once.
The multi-die design of many CPUs also penalizes them in AI environments, because accessing data across dies introduces latency, Ju argues. CPUs with cores and cache on different dies suffer latency when data has to cross between them. Maintaining cache coherency exacerbates that problem.
Finally, there's a security risk: a lack of hardware level sandbox isolation on some CPUs make it possible for prompt injection to reach core systems, he warns.
Customers also bring their own challenges as they grapple with different applications for AI. Enterprises evaluating AI server options have to match their server platforms to the projected AI workload profiles, Ju adds. Training a supervised learning model to batch-classify insurance claims takes different technical resources to running a low-latency reasoning employee chatbot.
ASUS wants to help customers solve problems like these wherever they come up. With this in mind, the company had plenty of goodies for onlookers at GTC.
Starting at the datacenter, the company announced both single and full-rack systems built around the NVIDIA HGX Rubin NVL8 eight-GPU server board. It has a 3U hybrid-cooled system, the XA NR11-E12L, along with the 2U 100 percent liquid-cooled XA NR11-E12, both targeting large AI reasoning workloads.
The two Rubin units target different use cases. The 3U hybrid liquid cooling system uses direct-to-chip liquid cooling on the 8GPU baseboard, along with air-cooled dual Xeon 6 chips on a sled. The company pegs this as the easy transition path into AI factory hardware. The 2U unit, with its 100 percent liquid cooling, is tailored for maximum PUE and compute density. It also uses a cable-free board-to-board design to keep its footprint low.
The kit will be speedy, too, with NVIDIA HGX Rubin offering 800Gbit of integrated bandwidth per GPU.
ASUS is promoting the serviceability of these units through their use of tool list CPU and GPU sleds. It claims up to a 50 percent time saving on maintenance operations for these units, meaning less downtime per service and ultimately a lower total cost of ownership.
ASUS is structuring its hardware proposition around speedy delivery so that customers can get hardware up and running to support their AI initiatives. That speed comes from validation carried out before delivery with the help of a pre-validated rack reference architecture.
In keeping with its focus on multiple designs for the right AI workload, ASUS also supports NVIDIA's modular MGX Blueprint for server builds. The selling point here is flexibility with a mixture of GPUs, CPUs, and networking options for enterprise inference, agents, and visual computing. This is the modular route for workloads that don't need the power of an 8-GPU HGX baseboard, and it covers a large gamut of enterprise inference and agentic use cases.
ASUS showcased MGX servers including the 6U XA P8A-E14AXL with its eight liquid-cooled NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. A 2U sibling, the XA P4N-E2, sports NVIDIA Vera processors for agentic work.
The ASUS booth also featured the ExpertCenter Pro ET900N G3, powered by the GB300 Grace Blackwell Superchip developer desktop with 748GB of coherent memory, and the Ascent GX10 based on NVIDIA DGX Spark for agentic work. Also on show were the ProArt GR1X Mini PC and ProArt P16 and P14 laptop - the first laptops powered by NVIDIA RTX Spark - aimed at both developers and creators.
For edge installations, it showed the PE3000N, its rugged NVIDIA Jetson T5000-based edge-AI computer aimed at use cases including industrial environments. Announced a year ago, this delivers up to 2,070 FP4 TFLOPS of peak AI performance with 128GB of memory. Targeted at industrial AI environments was the PE6000G, a workstation-grade AI and simulation platform.
Equipment only supports an AI factory when there is power to run it. ASUS has been taking steps with NVIDIA as a partner here, adopting its DSX AI Factory platform with digital twin system for laying out hardware and power in AI factory environments. DSX supports equipment using the NVIDIA Vera Rubin processor line, which supports dry coolers and a lower PUE with a 45 degree centigrade warm water inlet.
ASUS configured racks become simulation-ready assets inside DSX, adopting its OpenUSD shared file format. That covers engineering data alongside the geometry for measuring real power draw and cooling requirements.
ASUS wanted to target cooling as part of the original design plan for the AI Factory, not as a facility afterthought. So, the DSX partnership allows implementation teams to model water temperature, flow, pressure, CDU placement, and piping within the digital twin model. They translate information from the planning resources that go around this, such as the NVIDIA Omniverse DSX Blueprint framework, using the ASUS Infrastructure Deployment Center.
The digital twin system isn't just for design and setup. It's supposed to keep working after go-live so that AI factory operators can simulate changes before touching hardware. The ASUS Control Center monitors the infrastructure while DSX exchange links IT and operational technology data. Operators can validate changes in the simulation before flipping the switch to go live.
ASUS' goal here is clear: to assist enterprise customers as they tackle in-house AI opportunities while acknowledging that the performance profile and physical locations of their workloads will vary. As companies continue to evolve their understanding of AI and where it fits within the organization, they will need to be agile in how they plan and nurture their infrastructure from the datacenter out to the edge, and to the developer's desk.
The Berlin announcements tie it together in a neat package for organizations that need help with their agentic aspirations.
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