This work presents Pallas, a \textit{proactive} KV-cache migration framework that prepares the inference state at the predicted target before handover, in parallel with ongoing source-side inference and token delivery.
Abstract
AI-RAN brings large language model (LLM) serving close to mobile users, but cellular handover can separate an active request from its inference state: the user attaches to a target base station (gNB) while the large and growing key-value (KV) cache remains at the source. Retaining inference at the source preserves service continuity but persistently increases inter-token latency (ITL), whereas recovering the state at the target restores serving locality but requires KV-cache transfer, recomputation, or a combination of both only after handover, directly prolonging service interruption time (SIT). This work presents Pallas, a \textit{proactive} KV-cache migration framework that prepares the inference state at the predicted target before handover, in parallel with ongoing source-side inference and token delivery. At the preparation trigger, Pallas partitions the token sequence into a stable historical prefix and an evolving suffix. The target reconstructs the prefix through local prefill, while the source streams the KV blocks generated for the suffix. At handover, the target assembles both portions into an up-to-date KV cache and resumes decoding locally, leaving only unfinished preparation to contribute to SIT. An online scheduler selects the \textit{prefetching window}, which determines how early preparation begins before handover, based on mobility predictions and runtime telemetry. Across three LLMs and $100$--$500~\mathrm{Mbps}$ inter-gNB links, our vLLM-based prototype reduces average SIT by factors of $2.28$--$89.68$ over target-side recovery approaches and lowers average ITL by $16.0\%$--$50.0\%$ compared with source-side forwarding.
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 argues that future inference infrastructure should allow decoupling of compute and KV Cache storage across cloud and datacenters, and proposes a vision for an Internet for the KV Cache, with KV Cache management working as a content-distribution system.
Siddhant Ray, Nick Feamster, Junchen Jiang· 0 citations
Disaggregated LLM serving separates prefill and decode into distinct node pools, interposing a network fabric between the moment a key-value (KV) cache is computed and the moment it is consumed. This architectural shift invalidates a core assumption of classical cache policies: that the cost of a miss is simply recomputation on the same device. In disaggregated systems, a miss triggers both recomputation on a prefill node and a network transfer of the resulting KV block to the decode node—costs that differ by an order of magnitude and depend on prefix length, model width, and fabric bandwidth. Meanwhile, admitting a block to the global KV pool requires an additional transfer at compute time, so a poorly chosen keep decision wastes both memory and bandwidth even before reuse occurs. We present KVLearn, a learning-based retention framework that makes keep/evict decisions as first-class cost-optimization choices in disaggregated LLM serving. KVLearn consists of three components: (i) a lightweight Prefix Reuse Predictor (PRP) that estimates reuse probability from structural and temporal prefix features without touching model weights; (ii) a Cost-Aware Retention Score (CARS) that translates reuse probability into a keep/admit signal by accounting for per-block recompute, transfer, and storage costs; and (iii) an Adaptive Threshold Controller (ATC) that adjusts the admission threshold online using closed-loop feedback from observed hit rates and memory pressure. We integrate KVLearn into a globally disaggregated serving topology and evaluate it on both text and multimodal workloads, where image/video-derived tokens create large, expensive-to-recompute KV blocks under heterogeneous reuse distributions. KVLearn reduces end-to-end time-to-first-token (TTFT) by up to 56% vs. No-Cache (recompute-only), up to 38% vs. LRU-Pool, and up to 33% vs. Mooncake-style disaggregated baselines. Inter-node KV transfer volume is cut by up to 53% vs. LRU-Pool. On MM-Session, throughput stays within ~5% of oracle. Our code implementation of KVLearn is available at https://github.com/FastLM/KVLearn.
Dong Liu, Yanxuan Yu, Eric Jiang et al.· Proceedings of the 19th ACM...· 0 citations
Large language models are increasingly composed into agent loops that plan, call tools, and resume the same task after each action. These loops press a shared memory hierarchy harder than conventional multi-turn chat, because they hold a growing key-value (KV) prefix across tool waits and place many sessions on one SRAM/HBM pool, so that eviction and hierarchical placement become a session-level efficiency problem orthogonal to compute-mode optimization. Existing proxies based on recency, timeout, or identity miss the mechanism information of the loop and therefore treat a live wait as a cold, discardable unit. We present Unified Native Inter-turn Session Orchestration Nexus (UNISON), an event-driven near-memory scheduler in which Survival-Penalty Eviction for Agent Return-gap (SPEAR) and Tiering in Idle-window DMA Events (TIDE) share one live ranking. SPEAR selects who leaves from a gap average and a turn-indexed hazard, while TIDE spends the observed wait as a DMA budget for who sits in the fast tier. On coding and general-mission benchmarks with three model families, totaling 1,415 sessions and 33,596 turns, the joint policy is the best non-oracle entry on every trace, raising hit rate by 0.3% to 23.1%, reducing AMAT by 22% to 51%, and lowering TTFT by 58% to 89% on long-horizon traces. A structural necessity analysis shows that the unified near-memory design cannot be decomposed into independent IPs or realized in software without re-introducing documented failure modes. The 28-nm CMOS scheduling core occupies 0.169 mm^2 at 13.6 mW and 150 MHz, a negligible overhead relative to the KV hierarchy it manages, reproducing the floating-point ranking at Kendall tau exceeding 0.998.
SmartGen is designed, a KV cache transfer engine that allows seamless disaggregated LLM inference with three data transfer paths that reduces time-to-second-token by up to 4.3x compared with the typical full KV cache transfer approach while offering comparable subsequent decoding performance and accuracy.
Xuchuan Luo, Jiacheng Shen, Xin Wang et al.· arXiv.org· 2 citations· ⚡1
LLM serving is increasingly accelerated by position-independent caching (PIC). Existing PIC methods, however, are built for full-attention models, where a token-indexed KV cache underlies its core operations: matching reusable token chunks, concatenating their KV entries, and selectively recomputing a few tokens to restore cross-chunk context. Hybrid LLMs break these primitives---they replace most attention layers with linear recurrences that expose only a fixed-size state, leaving no token-indexed KV to concatenate or to locally repair. This raises a natural question: can PIC benefit hybrid models, and what would it take? We present LinearKV, a training-free hybrid-PIC framework. Its key insight is a \emph{decoupled initialization}: each linear layer maps its $K$ matched local states to a single initial state, while full-attention layers concatenate their KV as before. LinearKV is therefore compatible with existing PIC methods, reusing their token selection and recomputation as-is. Under this framework, we find that a \emph{single cached state} suffices as the linear layer's initializer. The algebraically principled alternative---composing all $K$ cached states into the exact full-prefix state, as concurrent work HYPIC does---is unnecessary and, on some architectures, even harmful. We compare the two across three hybrid models and three PIC selectors. On the two GDN models the two tie, both recovering most of full quality (up to $92\%$); on the Mamba-2 model, exact composition instead collapses under every selector---under EPIC, for instance, it recovers only $46.6\%$ of full quality, versus $86.8\%$ for a single cached block initializer. A single state initializer is also cheaper, cutting time-to-first-token to $0.46\times$ full prefill versus a further $5$--$17\%$ overhead for exact composition; results hold across LongBench QA and RULER at 8K--32K.
Yi-Rui Liu, Ruoling Qi, Long-Wen Wang et al.· 1 citation
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