Scaling AI Inference with KV Cache Offloading: Why Storage Is Becoming a Key Enabler for Next-Generation AI Systems

The emerging bottleneck in AI inference

As large language models (LLMs) continue to scale, the focus of AI system optimization is gradually shifting. While training remains resource-intensive, inference is increasingly becoming the dominant workload―particularly in agentic AI environments, where models interact continuously, maintain conversational context, and generate outputs across multiple stages and agents.

In these scenarios, inference is no longer a simple operation. It depends heavily on maintaining and reusing contextual information stored in key-value (KV) cache. As model sizes grow and inference workflows extend across distributed, multi-node systems, KV cache must persist beyond a single request or device. This places increasing pressure on memory capacity and data locality, which begin to constrain overall system scalability.

As a result, a new bottleneck emerges in AI inference pipelines. Efficiently persisting, accessing, and reusing KV cache―without overwhelming system memory or compromising responsiveness―has become a system-level challenge. Addressing this challenge increasingly requires rethinking how memory, storage, and compute are organized across the inference stack.

Why KV cache offloading matters

Recent developments point to a clear architectural shift in AI infrastructure. NVIDIA’s introduction of CMX™ (Context Memory eXpansion) in the Vera Rubin platform—along with the adoption of Samsung’s PM1753 enterprise SSD—demonstrates that extending memory capacity beyond GPU-attached limits is no longer conceptual, but actively implemented at the system level.

As KV cache–driven inference state continues to scale, keeping all data within GPU or system memory is becoming increasingly impractical. This is driving the need for more flexible memory hierarchies that can sustain data reuse across sessions, agents, and devices.

KV cache offloading addresses this by introducing a storage-backed layer into the inference memory stack. By selectively moving cache data outside of primary memory, it reduces pressure on constrained resources while maintaining efficient reuse across inference steps.

This becomes particularly relevant in read-intensive workloads that repeatedly access large context data—where storage performance directly impacts end-to-end inference efficiency.

Understanding the workload characteristics

To better understand how these architectural changes manifest in real systems, Samsung conducted system-level evaluations using PM1753 in representative AI inference environments. The goal was to observe how inference workloads interact with storage as KV cache data is offloaded and reused across GPUs.

One clear observation is that KV cache offloading is driven by the movement of large chunks of data, rather than frequent small I/O operations. As inference sessions shift across GPUs, previously generated context is transferred and reused in sizable chunks. This shifts the role of storage toward sustaining high-volume data delivery, rather than handling fragmented access patterns.

Overall, KV cache offloading workloads are predominantly read-intensive and exhibit bursty behavior under concurrency. This places critical demands on storage systems to deliver high throughput and parallel access while maintaining consistent latency.

What performance and efficiency data tells us

Samsung’s evaluations indicate that KV cache offloading, when paired with high-performance storage, can meaningfully improve inference scalability. Rather than optimizing a single metric, the approach influences overall system behavior across performance, power efficiency, and operational cost.

At the system level, offloading KV cache reduces memory pressure and avoids repeated computation during inference, helping maintain stable latency as concurrency increases. By shifting part of the workload from compute to storage, GPU resources are used more efficiently, enabling higher throughput and more consistent response under load.

Implications for scalable AI infrastructure

The growing importance of KV cache offloading points to a broader shift in AI system architecture. As inference workloads become more interactive and distributed, storage is evolving from a supporting component into an active enabler of system scalability.

Samsung’s evaluation with PM1753 illustrates this transition in practice. Storage capabilities such as high throughput, low latency, and consistent performance under parallel access allow it to play a meaningful role in inference workflows, rather than becoming a bottleneck as scale increases.

Looking ahead, KV cache offloading is likely to become a foundational consideration in next-generation AI infrastructure design. How storage, compute, and system architecture are balanced will increasingly determine how effectively AI services scale in real-world deployments.

Learn more

For a deeper dive into the detailed evaluation results, workload analysis, and system-level measurements based on real hardware configurations, we invite you to download and read the full whitepaper^[1]:  Download

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