AdaptiveKV: Accelerating KV Cache Offloading with a Bandwidth-Adaptive Memory Allocation Mechanism

Elastic Memory Remapping for Multi-tenant LLM Serving

This work presents Oneiros, a dynamic remapping engine for multi-tenant LLM serving that dynamically repurposes GPU memory allocated for model parameters as KV cache capacity, enabling nonblocking, unidirectional parameter transfer.

OasisKV: Scaling In-Decode KV Cache Beyond HBM with Lookahead Sparse Prefetching

OasisKV is presented, a memory-centric LLM inference system design that alleviates HBM capacity pressure by decoupling full KV-cache storage from HBM during LLM decoding and observes that future important tokens can be predicted accurately in advance using lookahead tokens drafted by speculative decoding (SD).

FlashAccel: Leveraging High-Bandwidth Flash (HBF) for High-Throughput LLM Inference

FlashAccel integrates HBF into HBM-based GPUs, providing architectural support to mitigate access latency and introduces an HBF-aware storage management layer together with a programming model to organize persistent data in HBF and coordinate heterogeneous memory resources at the system level.

Understanding and Optimizing KV-cache Management for Long-Context LLM Inference A

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

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.

Scaling Attention Beyond GPUs for LLM Inference

Scaling inference for large language models is increasingly constrained by limited GPU memory, primarily due to the expanding intermediate states (KV caches) required for long-context generation and multi-user workloads. Once the KV cache exceeds the capacity of high-bandwidth memory, it must be offloaded to host memory and reloaded on demand, a workflow severely bottlenecked by the CPU–GPU interconnect, typically PCIe. Existing approaches exploiting offload KV caches to CPU memory and selectively reload partial segments for attention computation often underutilize CPU compute resources and suffer from accuracy degradation. We present Beyond, a drop-in runtime that integrates a smart offloading scheme to selectively identify and retain salient KV entries across continuous decoding sessions, together with a hybrid CPU–GPU attention mechanism for scalable inference. Beyond executes dense attention over recent KV entries stored in GPU memory while performing parallel, per-head sparse attention on salient contextual KV entries residing in CPU memory. The outputs are fused efficiently through a log-sum-exp scheme. During the bandwidth-constrained decoding phase, oversized KV caches are processed cooperatively by the aggregated CPU and GPU memory bandwidth, with only minimal PCIe data movement. Experiments across diverse models and workloads demonstrate that Beyond improves scalability, supports longer sequences and larger batch sizes, and outperforms existing sparse attention baselines in both efficiency and accuracy—all on commodity GPU hardware.