Bounded-State Restoration: Decoupling Local Restore Capacity from External LLM State
On the map: Efficient LLM inference and serving → KV cache optimization
Abstract (arXiv)
Hierarchical KV-cache systems can retain long-context LLM execution state beyond GPU memory, but retention capacity does not determine the local memory required to make that state executable again. We isolate this second resource as the restoration working set (RWS): the peak local staging state whose lifetimes overlap during restoration. In the pinned upstream LMCache whole-plan path, measured full-reuse points for 1.956, 7.823, and 15.646 GiB/rank states first succeed at 2, 8, and 16 GiB L1 rungs, with successful L1 peaks of 1.956, 7.824, and 15.648 GiB/rank. We introduce Bounded-State Restoration (BSR), which separates complete discovery from local residency. BSR probes the complete reusable prefix without materializing the whole hit in L1, then installs confirmed state through a reusable window of at most W chunks. Under bounded auxiliary state, peak restoration capacity is O(W) while total transfer and installation work remains Theta(|S|). Because reusable state spans heterogeneous allocator groups and tensor-parallel ranks, BSR uses a request-level commit rule: partial installation is never exposed as a valid reusable prefix; failures invalidate the advertised prefix and fall back to a lower valid tier or deterministic recomputation. On DeepSeek-V4-Flash with TP=2 across two DGX Spark nodes, a clean no-resume sweep grows external state from 1.956 to 31.277 GiB/rank while measured L1 RWS remains exactly 500.75 MiB/rank at W=32, a 63.959x largest-state external-to-live-staging ratio. A second fresh 524K-token run repeats the largest-state acceptance result. Evaluated tier and rank-asymmetric failures expose either complete reuse or zero external reuse before fallback. A matched SSD optimization reduces 512K restore TTFT from 43.1 to 17.6 seconds without changing RWS.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in KV cache optimization, one of 12 directions in Efficient LLM inference and serving. Builds on 4 of its 8 references that are in the map. First posted on arXiv 18 Aug 2026.
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4 of 8 references.
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- 2025LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference8 Oct
- 2025Strata: Hierarchical Context Caching for Long Context Language Model Serving26 Aug
- 2024Fast State Restoration in LLM Serving with HCache7 Oct
- 2024Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention23 Mar
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- 2026CacheFlow: Efficient LLM Serving with 3D-Parallel KV Cache Restoration
- 2026CacheReforge: Bounded Recovery for Stale KV Caches under Evolving Adapters
- 2026From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Serving
- 2026Fast Recovery for LLM Serving via Decoupled Device Memory Lifetime in Dynamo
- 2026RestoreKV: Recovering Full-Cache Behavior Under Aggressive Query-Agnostic KV Cache Eviction
- 2026TempoKV: Timely Staging of LLM KV Caches for Memory-Semantic Flash
- 2026The KV Cache Is the New Memory Wall
- 2026When Fancy Eviction Fails: Rethinking Cache Replacement For LLM Prefix Reuse