KV Cache Reuse Strategy: Efficient Inference

• KV Cache Reuse Strategy is a method for reusing computed key-value tensors across overlapping input contexts to reduce redundant operations and improve scalability.
• It employs techniques like bidirectional scheduling and selective token recomputation to balance compute and storage, achieving marked improvements in throughput and latency.
• Practical deployments require dynamic recomputation rates, memory-aware compression, and tailored eviction policies to maintain high accuracy in multi-tenant and long-context scenarios.

A key-value (KV) cache reuse strategy refers to the design, algorithms, and system optimizations that enable LLMs and multimodal transformers to leverage already-computed key and value tensors across repeated or overlapping input contexts, thereby reducing redundant computation, prefill (prompt processing) time, memory usage, or total inference latency. Such strategies underpin scalability and efficiency in high-throughput serving, especially for long-context or multi-tenant deployments. Below, core principles, algorithmic advances, formal properties, and empirical results across system, model, and application levels are systematically presented.

1. Theoretical Foundations: Cumulative Error and Optimal Recompute

Modern transformer decoders rely on constructing per-layer, per-token key/value caches during autoregressive inference. KV cache reuse becomes nontrivial when the new input diverges (non-prefix reuse), leading to hidden state deviations that accumulate as tokens are reused unchanged in mismatched positions. VLCache formalizes the cumulative reuse error as:

$$e_k^{\text{total}} = e_k^{\text{self}} + \sum_{i=1}^{k-1} e_{i,k}^{\text{prop}},$$

where $e_k^{\text{self}}$ is the error from directly reusing token $k$, and $e_{i,k}^{\text{prop}}$ is the error propagated from upstream reused tokens. For exact-prefix (lossless) reuse, all error terms vanish.

Optimal recomputation to minimize total error requires prioritizing the earliest tokens, as their errors affect all downstream positions. Mathematically, recomputing early positions maximally cancels cumulative error for subsequent tokens. Empirically, for fixed recomputation budgets, static or late-token recomputation is consistently inferior (Qin et al., 15 Dec 2025).

2. System Architectures and Bidirectional Scheduling

KV cache reuse in high-throughput inference pipelines encounters unique system bottlenecks, particularly in prefix caching with storage hierarchies. The "Cake" system architecture exemplifies the juxtaposition of compute-bound and I/O-bound regimes. Rather than choosing to either load cached prefixes (which can be bottlenecked by storage bandwidth) or recompute (bound by GPU throughput), Cake employs bidirectional scheduling: a forward thread starts computing KV chunks from the start, while a backward thread loads cached chunks from the end. The meeting point in token space is dynamically determined by the relative speeds of computation and I/O:

$$\text{TTFT}_{\text{Cake}} = \max(\text{ComputeTime}_\text{front}, \text{I/O Time}_\text{back})$$

This parallelization achieves up to 2.6× TTFT reduction over one-sided approaches in large-scale deployments (Jin et al., 2024), and fully adapts to GPU or I/O resource fluctuations.

3. Selective Token and Layer-aware Recomputation

Layer-aware recomputation builds upon the insight that not all transformer layers require equal freshness of KV states. In VLCache, layerwise sensitivity is empirically quantified by the change in output logits (or end-task metrics) under selective token recomputation. The optimal per-layer recompute schedule

$$\min_{r_1, ..., r_L} \sum_{\ell=1}^L S_\ell(r_\ell) \text{ s.t. } \sum_\ell r_\ell \leq P_\text{tar}, \quad r_1 \geq r_2 \geq \dots \geq r_L$$

allocates higher recomputation rates to earlier (more sensitive) layers. The schedule is constructed via greedy or integer programming approximations, with dynamic strategies outperforming static uniform rates for fixed compute budgets (Qin et al., 15 Dec 2025).

4. Application Domains: Multi-modal, Multi-document, and Multi-tenant Reuse

Multimodal Vision-Language Pipelines

VLCache exploits hash-based storage and selective recomputation to reuse encoder (image/vision) and decoder-side KV caches across recurring multimodal inputs. Hashing at both the global (image) and per-image levels maximizes reuse granularity, with pipeline steps including:

• Hash and store image encoder features.
• Detect cache hits; reuse stored feature/KV blocks.
• Apply block-sparse masks in the LLM to recompute only a small fraction of tokens per layer.

End-to-end, this pipeline yields TTFT speedups ranging from 1.2× to 16× while preserving accuracy within 1 pt of full recomputation (Qin et al., 15 Dec 2025).

Document-level and RAG Systems

In RAG or retrieval-heavy settings, KVLink precomputes and stores each document's cache offline (without positional rotations), applies appropriate RoPE corrections at inference to align global token positions, and introduces trainable link tokens to restore cross-document attention. This achieves near 90% TTFT reduction, and outperforms prior block-stitching or prefix-caching schemes in QA accuracy and system throughput, with compression/quantization orthogonally applicable (Yang et al., 21 Feb 2025).

5. Memory- and Storage-Aware Policies: Compression, Eviction, and Reuse Metrics

Compression and Structural Redundancy

Frameworks such as KV-CAR and ThinKV combine autoencoder-based dimensionality reduction and head-level cache similarity checks, further leveraging thought-adaptive quantization or eviction policies, tuned to observed attention sparsity patterns. ThinKV, for instance, assigns 8/4/2-bit quantization to Reasoning/Execution/Transition segments (based on attention sparsity), evicts low-utility segments via clustering, and uses an in-place kernel to avoid expensive compaction.

Reuse and Eviction Analytics

Production characterization studies establish that cache reuse is highly skewed (10% of blocks serve 77% of hits) and that effective cache policies must account for both spatial locality (prefix offset) and temporal statistics—in Alibaba Cloud, real-world policies that forecast expected reuse yield 5–10% absolute hit-rate gains over LRU and 30–40% lower mean inference latency (Wang et al., 3 Jun 2025).

6. Empirical Results and Quantitative Impact

KV cache reuse strategies consistently demonstrate multi-fold system improvements. Key quantitative highlights include:

System/Technique	TTFT Reduction	Accuracy Loss	Cache Savings	Throughput Gain	Reference
Bidirectional Cake	2.6×–11.8×	≈0%	–	Up to 95%	(Jin et al., 2024)
VLCache Dynamic	1.2×–16×	<1 pt	–	–	(Qin et al., 15 Dec 2025)
KVLink (RAG)	~10×	0–1%	–	–	(Yang et al., 21 Feb 2025)
ThinKV (Adaptive)	Up to 5.8×	<4%	>95%	>5×	(Ramachandran et al., 1 Oct 2025)
MemShare (Zero Copy)	66–84%	<5%	Up to 40%	>50%	(Chen et al., 29 Jul 2025)
Real-world eviction	28–41% (QTTFT)	–	–	–	(Wang et al., 3 Jun 2025)

Accuracy-optimized strategies (ProphetKV) further show that with 20% recomputation budgets, full-prefill performance is retained at a fraction of the compute time, outperforming prior salience- or cache deviation-based methods by wide margins (up to 50% accuracy improvement on LongBench) (Wang et al., 31 Jan 2026).

7. Practical Deployment Guidelines and Limitations

For robust, economical KV cache reuse in contemporary LLM serving, best practices include:

• Integrate fast hash or embedding-based cache lookup to support both exact and near-prefix matches.
• Tune recompute rates per layer and per token segment to balance quality and resource usage, based on empirical sensitivity curves.
• In storage-constrained settings, employ per-entry lossy compression with offline profiling and knapsack-based optimization to maximize DRAM hit rates and minimize loading delays (Feng et al., 28 Aug 2025).
• For real-world cloud workloads, profile true reuse time and prefix offset distributions to inform eviction—treating all blocks identically via recency or frequency alone is provably suboptimal (Wang et al., 3 Jun 2025).
• In scenarios requiring precise token-level correctness (e.g. infilling, multi-candidate judging), explicitly consider cross-chunk or cross-candidate attention interactions, as unsophisticated reuse disrupts selection invariance and can bias downstream outputs (Liang et al., 13 Jan 2026).

Complex deployments should incorporate hybrid policies, including partial recomputation, case-by-case controller modules, and modular plug-ins (e.g. logger, statistical profiler, cache compressor) to adapt to shifting workload and memory regimes.

---

In summary, KV cache reuse strategies have evolved from simple prefix caching to sophisticated, theory-backed frameworks that span fine-grained dynamic recomputation, adaptive memory management, and zero-copy block sharing, with demonstrated utility across efficiency, memory, and accuracy dimensions in large-scale LLM and vision-language deployments (Qin et al., 15 Dec 2025, Jin et al., 2024, Yang et al., 21 Feb 2025, Chen et al., 29 Jul 2025, Wang et al., 31 Jan 2026, Ramachandran et al., 1 Oct 2025, Wang et al., 3 Jun 2025).