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

Jing Ding, Yash Nishant, Chandrish Ambati, Jyothsna Kamati 2026-07-31
Cache × LLM × Inference GPU Architecture Simulation Workload

The paper addresses the memory wall in LLM inference, where KV cache demands tens of terabytes at hundreds of GB/s exceed current memory tier capabilities. The proposed Marvell Photonic Fabric Memory Appliance replaces electrical switches with a passive fiber shuffle in a switch-free full-crossbar topology, delivering 32 TB shared memory across 16 hosts via photonic-CXL hybrid architecture. Emulation results show over 50% latency reduction versus electrical CXL pools, while simulation demonstrates a 6.6x improvement in time-to-first-token by eliminating cache eviction cliffs for multi-turn workloads. This work matters because it enables practical TB-scale shared memory for concurrent long-context users, overcoming the scalability limits of electrical CXL pooling in real deployments.

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InferScale: GPU-Native KV Injection for Personalized LLM Serving

Peter Li, Prashant Pandey 2026-07-31
Memory Microarchitecture Simulation GPU Cache × LLM × Serving

InferScale is a GPU-native LLM memory system that replaces repeated prompt prefilling with reusable KV state, addressing the TTFT increase caused by injecting persistent personalized context into prompts. It precomputes KV representations for memory facts, stores them with semantic embeddings on the GPU, and injects them directly into vLLM's paged cache, using Chunked RoPE for dynamic memory assembly and Context-Window Encoding to mitigate the loss of cross-fact context. On LoCoMo with three open-weight models, InferScale keeps TTFT nearly constant as retrieval budget grows, reducing TTFT by 72-79% (3.6-4.8x) at k=50, achieving 60.3% accuracy versus 63.3% for Mem0, and delivering 3.7-4.5x throughput under concurrent load. This decouples memory-conditioned serving latency from retrieved-context size while preserving application quality, enabling efficient personalized LLM serving without engine modifications or fine-tuning.

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