Composable CXL Memory as a Kubernetes-Native Shared Memory for LLM Serving

Abstract:We present a Kubernetes Dynamic Resource Allocation (DRA) driver that makes composable CXL memory a schedulable cluster resource, and evaluate the resulting shared-memory tier for cross-node KV-cache reuse in LLM serving. The driver composes CXL regions on demand, materializes them as DAX devices on each participating host, and injects them into pods under a single Container Device Interface (CDI) name so that pods on different nodes access the same physical region. A shared-memory connector for vLLM/llm-d uses that region as a KV-cache tier with a slot directory embedded inside the shared medium, which eliminates the need for an external metadata service. On a two-node cluster with a 512\,GiB CXL appliance and Qwen2.5-7B-Instruct, cross-node prefix reuse reduces TTFT by 5.5$\times$--36.6$\times$ at an external hit rate of 95.4--99.5\,\%, while node-local tiers (GPU prefix caching, CPU-DRAM offload) fall back to full recompute. The sharing gap, defined as the latency ratio between cross-node and same-node reuse, is 1--4\%, indicating that cross-node reuse incurs little additional latency relative to same-node reuse on our testbed. Both replicas run full engines; the study demonstrates memory disaggregation rather than prefill/decode disaggregation. We report this as a feasibility study rather than a performance evaluation.

Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)

C.5.5; D.4.2; B.3.2; C.2.4

arXiv:2609.10790 [cs.DC]

(or arXiv:2609.10790v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2609.10790 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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