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arxiv: 2606.24506 · v2 · pith:WSGHWPLFnew · submitted 2026-06-23 · 💻 cs.DC · cs.AI· cs.LG· cs.PF

CrossPool: Efficient Multi-LLM Serving for Cold MoE Models through KV-Cache and Weight Disaggregation

Zhuoren Ye , Tianyu Wo , Dinghao Xue , Mingming Zhang , Yuchen Teng , Chunming Hu , Renyu Yang This is my paper

Pith reviewed 2026-06-29 05:27 UTC · model grok-4.3

classification 💻 cs.DC cs.AIcs.LGcs.PF
keywords Multi-LLM servingCold MoE modelsKV-cache disaggregationWeight poolingGPU memory managementPipeline schedulerLong-context inferenceTail latency
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The pith

Disaggregating weights and KV-cache into separate GPU pools cuts P99 TBT up to 10.4x for cold MoE models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Cold MoE models receive sparse requests and remain cold, so reserving worst-case KV-cache per model wastes memory when weights and KV-cache share one pool. CrossPool splits them into a weights pool that consolidates FFN weights across models and a KV-cache pool that allocates based on aggregate active demand. A planner, virtualizer, layer-wise pipeline scheduler, and persistent kernels hide transfers and lower control overhead. This design improves memory utilization and long-context support under low-concurrency traffic. The system outperforms prior KV-cached multi-LLM serving by reducing tail time-between-tokens up to 10.4 times.

Core claim

CrossPool separates FFN weights into a consolidated pool and KV-cache into a dynamic pool. The weights pool holds stable model parameters across cold MoE instances while the KV-cache pool provisions only the current aggregate demand. A KV-cache planner and virtualizer manage allocation, a layer-wise pipeline scheduler overlaps hidden-state movement, and persistent kernels with control lowering cut CPU-GPU overhead, allowing attention to stay local to the KV pool.

What carries the argument

The two-pool disaggregation of weights and KV-cache, managed by a KV-cache planner, virtualizer, and layer-wise pipeline scheduler that hides transfers.

If this is right

  • KV-cache capacity can be shared across models using total active demand instead of per-model peaks.
  • Attention computation stays local to the KV-cache pool, exposing more replicated capacity even at low concurrency.
  • Long-context requests become feasible without pre-reserving worst-case memory per model.
  • GPU memory utilization rises for the bursty, sparse request patterns typical of cold models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same separation could help dense models when KV demand fluctuates across instances.
  • Persistent kernels and lowered control overhead may reduce latency in other inference serving stacks.
  • Measuring exact transfer volume versus memory savings at different concurrency levels would reveal the operating range where the design wins.

Load-bearing premise

The cost of moving hidden states between the two pools plus the scheduler overhead stays small enough not to erase the gains from higher memory utilization under low-concurrency loads.

What would settle it

Run a low-concurrency workload with many cold MoE models and measure whether P99 TBT and throughput improve over a monolithic baseline; if they do not, the disaggregation benefit is offset by transfer costs.

Figures

Figures reproduced from arXiv: 2606.24506 by Chunming Hu, Dinghao Xue, Mingming Zhang, Renyu Yang, Tianyu Wo, Yuchen Teng, Zhuoren Ye.

Figure 1
Figure 1. Figure 1: Cold-model underutilization and accumulated KV￾cache usage under low RPS. Subfigure (a) summarizes data from OpenRouter [32], and (b) stacks active KV-cache bytes for four 7B models at 0.2 RPS over one hour. • KV-cache planner and virtualizer. CrossPool plans the shared KV-cache pool budget and parallelism offline, then exposes the pool through virtualized paging [28]. • Layer-wise pipeline scheduler. Cros… view at source ↗
Figure 2
Figure 2. Figure 2: KV-cache availability when serving a single re￾quest on 4 GPUs. Comparison of monolithic and disaggre￾gated memory pools for weights and KV-cache. n_heads values of MHA, GQA and MQA are 4, 2 and 1, respectively. the pool to a high percentile of aggregate demand instead of the worst-case load for each model. 2.2 Mismatch between Algorithms and Systems Recent LLMs use diverse attention algorithms (e.g., GQA … view at source ↗
Figure 3
Figure 3. Figure 3: CrossPool system architecture every pool crossing, for every layer, and for every generated token. Even though each transfer is much smaller than mov￾ing KV-cache tensors, the repeated transfers accumulate into non-negligible communication overhead. C3: Increased graph capture complexity under mixed scheduling. Modern serving engines rely on CUDA graph capture to reduce launch overhead, but disaggregated e… view at source ↗
Figure 4
Figure 4. Figure 4: Layer-wise pipeline scheduler. It interleaves atten￾tion and FFN layers of two batches, allowing attention and FFN to be executed simultaneously on different batches from different models. Early exit is supported when one batch finishes all its layers. CUDA VMM APIs [28] to reserve a virtual KV address range for each model and map physical KV pages on demand. At￾tention operators see a normal paged KV-cach… view at source ↗
Figure 5
Figure 5. Figure 5: Persistent kernels for efficient graph execution. across the pool boundary. CrossPool then uses persistent kernels and control lowering to keep frequent scheduling and communication control on GPUs, reducing host inter￾vention and CPU-GPU control transitions. Fig. 5b illustrates the design. CrossPool captures supported attention and FFN sub￾graphs during warmup and passes their graph handles to GPU-residen… view at source ↗
Figure 6
Figure 6. Figure 6: Maximum aggregate request rate estimated from sampled LongAlign context-length bins within each model’s nominal context window; vertical drops mark per-system capacity limits. Prompts are truncated to maximum context length of the two models [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Decode-side TBT on ShareGPT traces from 0.2 to 1.0 RPS per model. 5.2 Context-length Scalability [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
read the original abstract

Emerging LLM services increasingly host many sparse MoE models, yet most models receive sparse requests and remain cold. This creates a GPU memory problem: model weights are stable and model-determined, while KV-cache is transient and demand-determined. Because cold models rarely reach peak KV-cache demand at the same time, reserving worst-case KV capacity per model wastes memory; a shared KV-cache pool can instead provision aggregate active demand. However, KV-cache sharing is not sufficient when weights and KV-cache remain in a monolithic GPU memory pool. Static weights compete with dynamic KV-cache, and KV-head-limited attention under cold, low-concurrency traffic exposes only a fraction of replicated KV capacity, leading to low GPU memory utilization and weak long-context support. We present CrossPool, a serving engine for cold MoE models that separates FFN weights and KV-cache into two GPU memory pools: a weights pool that consolidates FFN weights across cold models, and a KV-cache pool that dynamically serves active requests while keeping attention local to KV-cache. CrossPool combines a KV-cache planner and virtualizer, a layer-wise pipeline scheduler that hides hidden-state transfers, and persistent kernels with control lowering to reduce CPU-GPU control overhead. With efficient GPU memory pooling, CrossPool underpins bursty long-context requests and outperforms the state-of-the-art kvcached-based multi-LLM serving system, reducing P99 TBT by up to 10.4x.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper presents CrossPool, a multi-LLM serving engine for cold MoE models that disaggregates FFN weights into a consolidated weights pool and KV-cache into a separate dynamic pool. It introduces a KV-cache planner/virtualizer, a layer-wise pipeline scheduler to overlap hidden-state transfers, and persistent kernels with control lowering. The central empirical claim is that this design outperforms the state-of-the-art KV-cache-based multi-LLM serving system, reducing P99 TBT by up to 10.4x while better supporting bursty long-context requests under low-concurrency traffic.

Significance. If the performance results hold under representative workloads, the disaggregation approach could meaningfully improve GPU memory utilization for sparse MoE deployments where monolithic pooling wastes capacity on worst-case KV reservations. The engineering focus on hiding cross-pool transfers via scheduling is a practical contribution to systems for cold models.

major comments (1)
  1. [Evaluation] Evaluation section (and associated figures/tables reporting the 10.4x P99 TBT result): the central claim that disaggregation delivers net gains rests on the layer-wise pipeline scheduler successfully masking hidden-state transfer latency. The manuscript should include an explicit ablation or per-layer timing breakdown (e.g., transfer time vs. FFN/attention compute time) under the low-concurrency, sparse-request workloads that define the motivating case; without this, it is not possible to confirm that the reported speedup is not offset by cross-pool movement costs.
minor comments (1)
  1. [Abstract] Abstract: experimental setup details (workload traces, hardware configuration, baseline implementation, and overhead measurements) are absent, making it difficult for readers to assess the 10.4x claim without immediately turning to the full evaluation section.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our evaluation. We address the concern about substantiating the layer-wise pipeline scheduler's effectiveness below.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section (and associated figures/tables reporting the 10.4x P99 TBT result): the central claim that disaggregation delivers net gains rests on the layer-wise pipeline scheduler successfully masking hidden-state transfer latency. The manuscript should include an explicit ablation or per-layer timing breakdown (e.g., transfer time vs. FFN/attention compute time) under the low-concurrency, sparse-request workloads that define the motivating case; without this, it is not possible to confirm that the reported speedup is not offset by cross-pool movement costs.

    Authors: We agree that an explicit ablation study and per-layer timing breakdown would provide stronger evidence that the reported speedups are not offset by transfer costs. In the revised manuscript we will add a dedicated subsection (and associated figure) presenting per-layer measurements of hidden-state transfer time versus FFN/attention compute time, plus an ablation comparing the full system against a variant without the layer-wise scheduler, all evaluated under the low-concurrency, bursty long-context workloads that motivate the work. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical systems paper with no self-referential derivations

full rationale

The paper describes a serving architecture (disaggregated weights/KV pools, KV planner, layer-wise pipeline scheduler, persistent kernels) and supports its performance claims (up to 10.4x P99 TBT reduction) via experimental comparisons against baselines. No equations, fitted parameters, or derivations appear that reduce to their own inputs by construction. No load-bearing self-citations or uniqueness theorems are invoked. The central claims rest on measured overheads and utilization gains under cold MoE workloads, which are externally falsifiable via replication on the described hardware.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a systems paper; no mathematical free parameters, axioms, or invented physical entities are introduced. The design rests on standard GPU memory management assumptions and the empirical observation that cold-model KV peaks do not coincide.

pith-pipeline@v0.9.1-grok · 5820 in / 1172 out tokens · 20574 ms · 2026-06-29T05:27:35.747557+00:00 · methodology

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