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arXiv · 2607.27187

A Photonic-CXL Memory Appliance for Scalable KV Cache Management in LLM Inference

Jing Ding·Yash Nishant·Chandrish Ambati·Jyothsna Kamati·Trung Diep
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Abstract

LLM inference at scale faces a memory wall. The KV cache demands tens of terabytes at hundreds of gigabytes per second, yet no current memory tier delivers both at once. Characterization across multi-generation GPU systems with various LLaMA models shows host memory retrieval achieves up to 100x speedup over re-computation but supports only tens of concurrent long-context users. Electrical CXL pooling theoretically bridges this gap, but switch latency, cable reach limits, and power-scaling issues prevent practical TB-scale deployments. We present the Marvell Photonic Fabric Memory Appliance, a photonic-CXL hybrid architecture replacing electrical switches with a passive fiber shuffle to deliver 32 TB shared memory across 16 hosts via a switch-free full- crossbar topology. Emulation results demonstrate over 50 percent latency reduction versus electrical CXL pools. Simulation results show that the PF Memory Appliance eliminates cache eviction cliffs by improving time-to-first-token by 6.6x for multi-turn conversations workloads.

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Jing Ding, Yash Nishant, Chandrish Ambati, Jyothsna Kamati, Trung Diep. 2026-07-29. A Photonic-CXL Memory Appliance for Scalable KV Cache Management in LLM Inference. https://arxiv.org/abs/2607.27187

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