Executive Summary

The rapid growth of generative AI has led the market, over the past two years, to focus on memory supply and storage capacity. As AI systems move decisively into an inference-driven phase, however, the fundamental bottlenecks facing infrastructure are beginning to shift.

In inference environments, system costs are no longer determined primarily by model size or total data volume. Instead, they are shaped by how contextual states persist during computation. When large volumes of context occupy high-cost memory tiers for extended periods, the true constraint is no longer raw compute power. It becomes whether unit inference costs can decline as the number of users scales, rather than rising in direct proportion.

As a result, AI inference infrastructure is gradually moving away from a growth model centered on capacity expansion. What may ultimately be repriced is not HBM or storage devices alone, but whether cloud providers and GPU platforms can establish an AI factory efficiency model that is sustainable and predictable over time.

Introduction: The Real Challenge Is Not Content Growth, but How Inference Systems Operate

Discussions of AI and data centers often begin from the same assumption. Generative AI is driving unprecedented growth in content, especially video. From corporate training and marketing materials to customer support tutorials and personal creation powered by AI, data volumes are clearly expanding at a rapid pace. It is therefore natural that storage capacity has become a central focus of the conversation.

Yet a closer look at how AI systems operate in the inference era reveals that the pressure does not come solely from the total amount of data being produced. What makes inference systems expensive and difficult to scale is often not whether data can be stored, but whether it can be retained during computation and reused when needed.

During inference, models do not simply read data once and terminate. As multi-turn conversations, long-context interactions, and reasoning workflows become common, systems accumulate large volumes of intermediate states to preserve semantic continuity and reasoning context.

These intermediate states are not the text visible to users. They are internal contextual memories within the model, typically stored as Key Value Cache, commonly referred to as KV Cache. They directly affect inference latency, the number of users that can be served concurrently, and the effective utilization of GPUs.

As context is retained for longer periods and reused more frequently, the nature of data itself begins to change. It is no longer written once and stored statically. Instead, it moves continuously across tiers with different speeds and cost structures.

In other words, the storage challenge in the AI inference era is gradually shifting from how long data should be kept to where it should reside within the system. Storage is increasingly becoming a question of memory hierarchy design, rather than a problem of long-term retention alone.

The Divergence of Two Infrastructure Roles

Before examining why KV Cache has become an increasingly influential factor in system design, it is necessary to distinguish between two infrastructure logics that now coexist in the age of AI inference, even as their roles continue to diverge.

The first is the data preservation architecture established during the cloud and big data era. In this model, data is treated as the most valuable asset. The system’s primary task is to reliably collect information, write it to storage devices, and retain it over long periods of time. Compute resources are allocated on demand to analyze and process existing datasets.

This design assumes that data remains largely static. It may be read repeatedly, but it rarely changes during computation. As a result, system challenges are concentrated on capacity expansion, data governance, and cost control.

The second is a data flow oriented architecture. It is not yet a fully standardized design, but rather an emerging structural trend. Here, the system is no longer centered on centralized storage. Instead, it is organized around a fabric of GPUs and high speed networks. Data is not stored statically for extended periods, but moves continuously through the system, where it is generated, consumed, and discarded across different nodes.

In such an environment, data access becomes extremely intensive. When intermediate states are frequently written to disks or SSDs, the latency created by data movement often translates directly into GPU idle time, quickly becoming a system level performance bottleneck.

As a result, new architectural approaches have begun to appear. These designs aim to keep data within memory layers whenever possible, offloading it to slower storage media only when necessary.

This shift also changes the role of storage itself. It is no longer the central asset of the system, but instead becomes a layered resource, increasingly functioning as an extension of the memory hierarchy.

Table 1. Core Differences Between Two AI Infrastructure Architectures

In a data persistence world, many problems can still be solved by adding more disks. In a data flow architecture, however, the truly expensive resource is no longer storage capacity, but GPUs sitting idle because data cannot arrive in time.

As long context windows and multi step reasoning become more common, demand for KV Cache is growing far faster than other system resources. These intermediate states can no longer be treated as negligible temporary data. Instead, they are becoming a central force pushing AI systems away from data persistence architectures and toward data flow oriented designs.

This shift does not mean that existing infrastructure logic is being replaced. Data persistence architectures have not failed, nor are they becoming obsolete. They remain essential for training datasets, enterprise records, and long term content retention.

What has changed is that a new layer has been added on top of this foundation. One is designed specifically for AI inference. The traditional data persistence architecture continues to manage data existence. The emerging data flow architecture begins to manage data behavior.

How KV Cache Reshapes the Division of Roles Between Memory and Storage

As AI systems enter the inference era, a new category of data has emerged. It is neither model weights nor user data. Instead, it consists of the contextual states generated during reasoning to preserve semantic continuity. These states typically exist in the form of KV Cache.

This type of data has several distinctive characteristics.

First, its scale is substantial. With long context windows, multi turn conversations, multi tenant services, and agent based workflows, contextual states continue to accumulate throughout the inference process. As a result, the total size of KV Cache grows far faster than most other system resources.

Second, it is frequently reused. Users often return to the same conversation, tasks are extended across multiple steps, and the same context may be retrieved again at different points in time.

Third, it does not require the long term durability associated with traditional enterprise data. Even if lost, contextual states can usually be reconstructed through recomputation. The central concern is not preservation, but how quickly the data can be retrieved when needed.

Finally, and most critically, when these contexts remain in GPU HBM for extended periods, they directly constrain the number of users a system can serve concurrently. As a result, expensive compute capacity cannot be fully utilized.

This creates a practical limit for inference systems. If all contextual states must reside in HBM, the cost of scaling inference services rises almost linearly with the number of users, making a sustainable operating model difficult to achieve.

For this reason, AI systems are beginning to require a new hierarchical logic, as illustrated in Table 2. Its purpose is not long term data retention, but to accommodate data that is transient yet large, reusable yet unsuitable for permanent residence in high cost memory. The goal is to strike a workable balance among latency, cost, and GPU utilization.

Table 2. The Differentiation of Memory and Storage Roles in the AI Inference Era

As data begins to be differentiated by usage cycles and functional roles, storage is no longer merely a question of capacity. It becomes part of system design itself.

Within this structure, storage does not disappear. Instead, it is reintroduced into the inference system as a component of the memory hierarchy. It is no longer simply the final destination for data, but a foundational resource that supports context movement, reuse, and scheduling.

Against this backdrop, some vendors have begun experimenting with new infrastructure layers designed specifically for inference context. One representative example is the ICMS concept proposed by NVIDIA. Rather than replacing traditional storage, ICMS seeks to provide a placement model better aligned with memory hierarchy logic for data such as KV Cache that sits between memory and storage.

ICMS refers to the inference context memory storage platform introduced by NVIDIA, built around BlueField 4. Its objective is to allow inference context to move out of HBM while remaining efficiently schedulable and reusable by the system. In this architecture, storage devices are only one component. What is fundamentally being reorganized is the position and role of context data within the overall memory hierarchy.

The core purpose of ICMS is not data persistence. It is to prevent KV Cache from occupying HBM for extended periods of time.

In other words, ICMS is less concerned with which device stores the data and more focused on where that data should reside during the inference workflow. Its value does not lie in improving the performance of any single storage component, but in enabling previously offloaded context to be restored to the appropriate tier when needed, without disrupting the execution rhythm of the GPU.

From NVIDIA’s platform narrative, this layer appears to serve as groundwork for a next generation inference centric system unit. At the same time, it may enter existing racks earlier as an add on component. This is why NVIDIA positions ICMS as a new form of infrastructure for the inference era. It sits between memory and storage, serving not the data itself, but the operational efficiency of the entire inference system.

Inference Pathways Are Rewriting the Default Logic of Data Movement

When the perspective is widened beyond any single product generation to the broader direction of compute architecture design, including GPUs and other AI accelerators, a shared trend becomes increasingly clear.

The core assumptions behind GPU architecture have been shifting in recent years. Systems no longer presume that data will continuously travel back and forth between compute nodes and storage layers built for long term retention. Instead, they aim to keep data as close to computation as possible, reducing latency, response time variability, and the cost of data movement.

This design language has gradually surfaced in NVIDIA’s architectural evolution. From strengthening internal memory hierarchies and data reuse mechanisms to incorporating interconnects and rack level design into the core system architecture, compute chips are moving away from the role of isolated execution units. They are becoming nodes within a broader network of data flow.

More importantly, this shift is not limited to a single vendor. AMD has made a similar choice with its MI300 series, placing memory at the center of system design. The emphasis is not on stacking more raw compute, but on preventing HBM from being occupied for extended periods of time. Memory is no longer treated as a passive resource to be consumed, but as a critical bottleneck that must be actively managed.

As chipmakers following different architectural paths converge on the same questions of where data should reside, how it should be reused, and how it should move across tiers, a clear signal begins to emerge.

The cost focus of AI infrastructure is shifting away from how data is permanently stored toward how data is placed and orchestrated during inference. As a result, data flow oriented architecture is no longer an interpretation drawn from isolated product strategies. It is increasingly becoming a structural observation about how inference systems must operate.

Returning to the Market: The Narrative Is Shifting

These structural changes are also reshaping how the market understands AI infrastructure.

In the early phase of generative AI, industry narratives were heavily centered on compute and memory supply. As model sizes expanded and GPU deployments increased, high bandwidth memory naturally became a critical resource. This logic still holds today. At least within training driven growth narratives, mainstream market discussions continue to focus on HBM supply expansion and whether storage capacity can keep pace with data growth.

As systems move into an inference driven phase, however, HBM is no longer used only for model weights and real time computation. An increasing share of memory capacity is occupied by context states. When KV Cache remains in memory for extended periods, the system is often constrained not by raw compute, but by how many users it can serve concurrently.

This creates a structural tension around memory. It remains indispensable, yet it is also the most expensive resource that must be conserved most carefully within inference systems. The question is not whether memory matters, but which data deserves to occupy the highest cost tier for extended periods of time.

Under these conditions, even if HBM supply becomes abundant, AI inference infrastructure cannot remain on a design path defined by simply adding more memory capacity. Such architectures cause inference costs to scale more closely with user volume, making it difficult for unit inference costs to decline over time. The core issue has never been whether HBM is sufficient, but which data should reside in HBM.

This usage driven constraint is what can be described as the memory ceiling. It is not the result of weakening demand, but of structural limits created by system design and operational behavior. Compared with narratives centered on supply, capacity, and production, these constraints have yet to become a primary focus of market discussion.

Within inference oriented architectures, the need for data retention and content preservation has not disappeared. Yet the real time inference path is gradually reducing its dependence on storage layers designed for long term retention. As systems write back to traditional storage less frequently, the market value of memory and storage begins to decouple from pure capacity growth and instead depends on whether they can be integrated into the system’s orchestration rhythm.

In this environment, the valuation logic for both memory and storage is being forced to evolve. Narratives based solely on capacity or performance metrics are steadily losing explanatory power. The key determinant of the cost curve is shifting away from component specifications toward how data is placed, retained, and scheduled across tiers.

As a result, the focus is no longer limited to compute or storage alone. It increasingly centers on how systems distinguish real time computation data from movable context states, and how new tiers and orchestration mechanisms are built around that distinction. This is why GPU architecture, interconnect design, inference software, and context management are now evolving in parallel toward the same direction.

AI infrastructure competition is therefore moving away from component level performance comparisons toward system level competition shaped jointly by cloud providers and GPU platforms. Evaluation criteria are converging on whether an AI factory can operate reliably over time. Even as headline investment levels continue to rise, the true drivers of the cost curve will be differences in orchestration efficiency and predictability.

As AI inference infrastructure gradually moves away from a growth model centered on capacity expansion, the market’s understanding of AI infrastructure is shifting as well.

From a market perspective, a deeper narrative is beginning to form. The focus of future repricing may no longer rest primarily on memory and storage themselves, but on which cloud providers and GPU platforms possess the system level capability to keep AI factories running stably while maintaining predictable inference costs.

Note: AI tools were used both to refine clarity and flow in writing, and as part of the research methodology (semantic analysis). All interpretations and perspectives expressed are entirely my own.