Aug 2026· 2026 International Conference on Intelligent Multimedia, Networking, and Security (IMNS)· pp. 1-6· 0 citations· 40 references
Abstract
KV-cache offload is widely used to stretch GPU memory for LLM serving, but its storage behavior has not been characterized at the block-device level. In this paper, we study LMCache through realworld multi-session workloads that span same/different context $\times$ same/different prompt, using over 100 stateless requests per workload. Our comparison is observational rather than factorial: it contrasts two realizable deployment snapshots—LMCache 0.3.0 with buffered I/O and the kernel page cache, and LMCache 0.3.16 with prefix-aware deduplication and O_DIRECT. Key findings define the paper. First, the legacy stack appears almost read-free at the SSD layer in all four workloads, but a flush_ram experiment shows that this is conditional on page-cache warmth: once the cache is evicted, the same path issues about 1 GB of sequential reads in 683 ms. Second, deduplication delivers a dramatic write reduction only for the exact-repeat workload: one set collapses from 1.62 GB to 11.75 MB of writes, while the other three sets remain in the 2–5 GB range. Third, O_DIRECT converts a hidden and bimodal read cost into an explicit and stable one: on one set, warm TTFT rises from 27 ms to 93 ms because each of the 99 warm requests re-reads the same ~12 MB partial chunk from SSD. The main conclusion is therefore not “newer is better,” but a system trade-off: predictability versus average-case latency.
The main result concerns the plain-load path: attained LDG bandwidth peaks at a small offered per-thread load (K ~ 2) and then declines, by about 35% from K=2 to K=8 at the authors' primary configuration.
Large language model serving faces a critical memory bottleneck: the KV cache grows with sequence length and batch size. PagedAttention uses fixed-size memory blocks to reduce allocator-level fragmentation, but recent KV eviction algorithms operate at a token granularity finer than block-level management. This mismatch causes intra-block fragmentation, leaving a large fraction of allocated KV memory unreclaimable. We present vToken, a lightweight token-level virtualization layer that decouples logical token liveness from physical block placement. vToken maintains a stable logical token view through token-table indirection and realizes physical reclamation by repacking live tokens asynchronously. The design preserves PagedAttention kernels and CUDA Graph compatibility. We implement vToken in vLLM and evaluate it with H2O, Random, and Scissorhands across models. Compared with a paired Naive-Evict baseline, vToken reduces retained KV blocks per request by 27.2\%--72.3\% and improves SLA-constrained throughput by up to 1.37$\times$. Under a constrained active-KV budget, it extends the maximum feasible concurrency by up to 2$\times$, while reducing the per-policy integration footprint from 500+ lines to under 50.
Yuanhang Gao, Xiangrui Yang, Yuanfeng Chen et al.· 0 citations
Results show that object-value signals rank what to retain, while persistent responsibility determines which group bears reclamation pressure, which shows that object-value signals rank what to retain, while persistent responsibility determines which group bears reclamation pressure.
An LLM serving engine sizes its key-value (KV) cache once, at startup, permanently setting aside a reserve for the worst-case prefill activation. During decode-dominant phases that reserve sits idle, yet it cannot be handed to the KV pool because it is exactly the memory a large prefill needs. We ask whether this reserve is reclaimable, and build a mechanism to test it. Our elastic KV cache lends the reserve to the KV pool during decode and returns it before prefill, driven by the scheduler's one-step-ahead view of the next batch. It is pure userspace on the CUDA virtual-memory path: two physical handles mapped into one contiguous virtual range per layer, so the attention kernel is unchanged and no driver patch is required. It decommits in a few milliseconds and recommits in tens of milliseconds, works with CUDA graphs and prefix caching, and never triggers an out-of-memory event. A static commit of the same memory is unsafe, crashing on prefill bursts, which makes the dynamic toggle necessary. Having built the mechanism, we test the premise it rests on and report an honest negative result. It only pays off if a small prefill chunk size badly hurts prefill latency. In a controlled experiment injecting long prompts into a live decode load, that penalty is small (median time-to-first-token differs by about 1% between chunk sizes of 8192 and 32768 tokens), because prefill is compute bound and decode consumes only about one token per sequence per step. Simply lowering max_num_batched_tokens recovers more KV than the controller does, at nearly equal latency. The reserve also dilutes under tensor parallelism, from 16% of KV at TP1 to 2.7% at TP4. We state precisely when reclaiming the reserve could still help, and release the mechanism as a reusable userspace elastic-VMM allocator.
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.
Thanks to notable performance and capacity advantages, NVMe SSDs promise an effective cache tier for alleviating the load pressure of back-end storage servers. Compared with DRAM, an NVMe SSD exhibits up to hundreds of times larger capacity but delivers two orders of magnitude smaller bandwidth per gigabyte. This paper reveals that these hardware characteristics challenge fundamental design goals of caches: (1) storing tons of objects in a large-capacity SSD easily induces severe, persistent fragmentation, resulting in low cache space utilization; (2) intensive front-end requests contend for limited SSD bandwidth, causing unpredictable cache lookup latency. To tackle these challenges, this paper presents Gemini. The core of Gemini is heat-informed, group-based object management with a hardware-assisted I/O prioritization mechanism. Specifically, we introduce a tree-based prediction model for agile object grouping and fast reaction to hotspot shift. It provides SSD-friendly, bulk object eviction with a zero-write, remapping-based migration mechanism. In addition, to ensure request performance, we propose a novel I/O model that mitigates bandwidth congestion and prioritizes their processing at the hardware layer. Evaluation results show that Gemini delivers up to 3.8<inline-formula><tex-math notation="LaTeX">${\boldsymbol{\times}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mo mathvariant="bold">×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="cai-ieq1-3711088.gif"/></alternatives></inline-formula> throughput improvement, 2<inline-formula><tex-math notation="LaTeX">${\boldsymbol{\times}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mo mathvariant="bold">×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="cai-ieq2-3711088.gif"/></alternatives></inline-formula> tail latency reduction, and 2.7<inline-formula><tex-math notation="LaTeX">${\boldsymbol{\times}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mo mathvariant="bold">×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="cai-ieq3-3711088.gif"/></alternatives></inline-formula> higher cache space utilization than state-of-the-art caches for both YCSB workloads and production applications.
