Artificial intelligence is evolving at an extraordinary pace. But behind the headlines about larger models and astonishing applications lies a less glamorous truth. AI systems are straining against the physical limits of their infrastructure. The most critical of these is the memory wall, a boundary where adding more compute no longer yields proportional gains because memory capacity, bandwidth, and latency cannot keep up. For AI application providers, where inference queries can exceed billions per day, the inability to scale memory independently from GPUs directly drives higher cloud infrastructure costs and reduced customer satisfaction, for example, end users will getting impatient if a chatbot takes 5 seconds or more to respond.
This bottleneck is not only a technical issue. It has serious implications for the economic and environmental sustainability of AI. The global AI inference market was valued at more than $76 billion in 2024 and is projected to reach over $250 billion by 2030. Inference servers alone are expected to generate more than $133 billion in revenue by 2034. At this scale, even small inefficiencies in infrastructure design translate into massive costs. Without rethinking how memory is integrated into inference workloads, the financial and energy demands of serving trillions of predictions could become unsustainable.
Why the Memory Wall Matters More Than Ever
AI’s growth has shifted the center of gravity from training to inference. Training may capture attention with massive GPU clusters, but inference is what makes AI useful in practice. Millions of queries, responses, and transactions happen continuously across global networks.
These workloads demand memory architectures that are both fast and abundant. Yet today’s approaches force organizations into difficult trade-offs.
- GPU-integrated memory is fast but scarce. High-bandwidth memory (HBM) typically tops out at around 100 GB per device. Industry revenue for HBM is expected to double to $35 billion in 2025, underscoring its growing role but also its high cost.
- System DRAM is plentiful but slow. Routing through CPUs introduces latency and bandwidth inefficiencies that undermine real-time performance.
Neither option satisfies the dual requirement of scale and responsiveness. GPU compute throughput has increased by more than 30 times over the past decade, while total available memory has grown only 2.5 times. The result is that organizations either overspend on GPUs simply to gain more memory or accept degraded performance in latency-sensitive applications such as fraud detection and natural language responses.
Decoupling Compute and Memory: A Shift in Architecture
The solution may not be bigger GPUs but a rethinking of the relationship between compute and memory. Emerging technologies such as Compute Express Link® (CXL®) and innovative switch fabrics like XConn’s Ultra IO Transformer (UIOT) are making this possible.
The Ultra IO Transformer (UIOT) is a next-generation CXL switch fabric that bridges PCIe and CXL seamlessly, without requiring CPU pass-through. It maps PCIe MMIO space directly to CXL.mem space, enabling PCIe devices(GPUs) to read and write into large CXL memory pools sharing memory resources dynamically. With support for CXL.mem interleaving, UIOT delivers higher bandwidth and lower latency memory access, all while requiring zero changes to existing PCIe device drivers.
By enabling GPUs to tap directly into large pools of disaggregated memory, UIOT breaks the dependency on CPU-managed access and limited onboard HBM. Instead of scaling compute and memory together, organizations can scale each resource independently, creating more flexible and efficient infrastructure. For NVIDIA’s Dynamo, it could leverage CXL memory pools to scale KV cache well beyond the 100s GB limit of HBM, enabling larger context windows, improved throughput, and reduced GPU overprovisioning.
This shift mirrors broader trends in data center design that favor modular and composable systems capable of adapting as workloads evolve. It also reflects the urgent need to optimize AI infrastructure investments. McKinsey estimates that global data center infrastructure will require 6.7 trillion dollars by 2030, with more than 5 trillion of that dedicated to AI workloads. Architectural efficiency is no longer optional.
Beyond Performance: Cost and Sustainability Implications
The impact of this architectural rethink goes well beyond raw performance. It changes the economics and sustainability of AI.
- Cost efficiency. Reducing the need for GPU overprovisioning lowers capital investment and operating costs.
- Energy efficiency. Avoiding redundant compute and unnecessary data copy/paste transported through networks reduce power draw and cooling requirements, cutting both emissions and utility bills.
- Longer infrastructure lifespan. With memory and compute decoupled, organizations can upgrade one without replacing the other, extending the useful life of existing hardware.
These are not incremental gains. UIOT enables seamless deployment of petabyte-scale CXL memory pools with deterministic low latency, directly accessible by GPUs. This transforms memory into a composable, shared resource—just as cloud has done for compute and storage. At scale, they could determine whether AI services remain economically viable in industries like healthcare, finance, and telecommunications, where real-time inference underpins critical operations.
The Road Ahead
AI adoption is accelerating so quickly that infrastructure design decisions made today will shape outcomes for a decade or more. Organizations that continue to rely on legacy architectures will face spiraling costs and limited flexibility. Those that embrace disaggregated memory solutions will be better positioned to support larger models, faster responses, and more sustainable growth.
The memory wall is real, but it is not insurmountable. By decoupling compute from memory, we can ensure AI continues to advance without running into financial and environmental dead ends. The future of AI infrastructure will not be defined by faster chips alone but by smarter ways of connecting them.
- About Jianping (JP) Jiang
- About Xconn Technologies
Jianping (JP) Jiang is the VP of Business, Operation and Product at Xconn Technologies, a Silicon Valley startup pioneering CXL switch IC. At Xconn, he is in charge of CXL ecosystem partner relationships, CXL product marketing, business development, corporate strategy and operations. Before joining Xconn, JP held various leadership positions at several large-scale semiconductor companies, focusing on product planning/roadmaps, product marketing and business development. In these roles, he developed competitive and differentiated product strategies, leading to successful product lines that generated over billions of dollars revenue. JP has a Ph.D degree in computer science from the Ohio State University.
Founded in 2020 by a team of veterans in Silicon Valley, our mission is to accelerate AI computing in data centers and HPC by introducing high performance, power efficient, scalable and cost effective interconnect solutions.
AI computing and data center architectures are undergoing a fundamental transformation of disaggregation and composability, driven by the enablement of CXL (Computing Express Link) technology.
The founders of XConn Technologies realized this vision from early on. We intend to have our product undertaking this important piece of the puzzle in the CXL ecosystem.
We are well funded. Our team members have multiple years of experience in the data center interconnect and switching field.
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