Externalizing KV cache: Architecting shared inference context for enterprise scale

[StorageExperts]

Externalizing KV cache boosts inference efficiency: RDMA/S3 storage enables fast, shareable retrieval, optimizing cost, performance, and accelerator use in multitier architectures.

As inference workloads mature, one constraint consistently surfaces: KV cache does not scale economically when confined to accelerator memory.

Recent platform direction from NVIDIA makes this explicit. Inference context is increasingly treated as a multitier resource rather than transient GPU state. As context windows expand and concurrency increases, repeatedly regenerating multigigabyte KV cache becomes structurally inefficient.

The architectural question is no longer whether KV cache can be externalized. It is whether retrieval can consistently outperform recomputation under production load.

Quantifying the problem

For a 32K token prompt using a Llama-3.1-8B-fp16–class model, prefill generates approximately 4 GB of KV cache.

At scale, this means each long-context request involves one of two operations:

• Regenerate ~4 GB of model state on the accelerator
• Retrieve ~4 GB of previously computed state from an external tier

Recomputation commonly measures in the range of 12–25 seconds, depending on hardware and concurrency. That time produces no new tokens and cannot be shared across inference workers.

In contrast, retrieval performance depends entirely on the datapath.

Datapath, not persistence

External KV cache is not primarily a storage durability problem. It is a data movement problem. An effective shared KV cache architecture must satisfy four conditions:

1. Retrieval latency must consistently beat recomputation.
2. Throughput must support multigigabyte transfers under concurrency.
3. Cache must be shareable across inference servers.
4. The datapath must remain predictable under load.

Traditional file systems and TCP-based object paths often fail on the first and fourth conditions. Local NVMe can satisfy latency but fails on sharing. This is why RDMA-backed object access becomes critical.

Empirical behavior under long-context load

In Hewlett Packard Labs testing of long-context inference scenarios, multiple KV cache back ends were evaluated for retrieving 0.5 GiB blocks representative of 32K context workloads.

The results show a clear hierarchy.

Table 1. Performance comparison of KV cache targets for long-context inference

KV cache target	Effective bandwidth	Load latency	Shared across hosts	Practical outcome
GPU recomputation (prefill)	N/a	12–25 seconds	No	Highest cost, worst efficiency
CPU memory (DDR5)	4–6 GB/s	10–30 ms	No	Fast, but severely capacity-limited
Local NVMe (PCIe)	3–7 GB/s	100–250 ms	No	Useful, but cache is stranded per host
HPE Alletra Storage MP X10000 (TCP/S3)	1–10 Gb/s	800–950 ms	Yes	Too slow to consistently beat recomputation
HPE Alletra Storage MP X10000 (RDMA/S3)	~100 Gb/s	250–400 ms	Yes	Faster than recompute, globally shareable

Using RDMA-backed retrieval from HPE Alletra Storage MP X10000, time to first token improvements of up to approximately 60% were observed across context lengths ranging from 1K to 32K tokens.

Equally important, accelerator time previously consumed by KV cache recomputation was reclaimed for serving additional requests.

Why multitier inference context matters

NVIDIA’s ecosystem direction increasingly reflects a memory hierarchy view of inference infrastructure: high bandwidth memory (HBM) at the top, system memory below, and network-accessible tiers extending capacity and sharing.

When KV cache can be retrieved in hundreds of milliseconds rather than regenerated in tens of seconds:

• Accelerator utilization improves materially
• Concurrency per GPU increases
• Power efficiency improves
• Cost per inference decreases

Inference shifts from being compute bound to datapath optimized.

Storage architecture implications

Supporting shared KV cache at scale requires storage systems designed for:

• High parallelism
• Low-latency object access
• Efficient RDMA data movement
• Predictable behavior under mixed inference loads

Platforms such as HPE Alletra Storage MP X10000 are architected with these characteristics in mind. Rather than positioning storage as a downstream repository, the design objective is to support inference-adjacent data movement as part of a distributed memory hierarchy. The difference is not feature-based. It is architectural.

The harder problem: Correctness under concurrency

The most subtle challenge in shared KV cache is not raw bandwidth. It is coordination.

Cache reuse must:

• Maintain deterministic mapping between prompt and KV blocks
• Avoid contention across inference servers
• Preserve isolation across tenants and workloads

This is where software layers such as LMCache and inference engines such as vLLM become essential partners in the architecture. Infrastructure provides throughput and sharing semantics. Software orchestrates lifecycle and reuse.

Neither layer can succeed independently.

From experiment to production architecture

External KV cache is often demonstrated in controlled environments. The enterprise challenge is sustaining its performance characteristics under:

• Variable prompt lengths
• Bursty concurrency
• Mixed workloads
• Cross-node reuse

When the datapath remains stable under these conditions, inference economics shift meaningfully. Recomputation becomes the exception, not the default.

Conclusion

The next phase of inference scaling will be defined less by accelerator count and more by how efficiently context moves through the system.

As multitier inference architectures become the norm, storage is no longer adjacent to inference. It becomes part of the memory hierarchy that determines whether reuse outperforms redundancy.

For enterprise AI platforms, that distinction will determine scalability, cost structure, and operational viability.

Learn more: hpe.com/us/en/alletra-storage-mp-x10000.html 

Meet the author:

Alex Veprinsky, Chief Architect, Storage, HPE