Dynamic KV-Cache Management

• Dynamic KV-Cache Management is a system that adaptively allocates and compresses key-value caches in transformer models based on token importance and activation uncertainty.
• It employs layer-wise and sequence-wise budget allocation techniques to significantly reduce memory usage while maintaining near-iso-accuracy and boosting throughput.
• The framework supports multi-GPU and multi-tenant deployments through advanced scheduling and storage strategies, leading to enhanced resource utilization and inference speed.

Dynamic KV-Cache Management refers to the class of algorithms, systems, and practices that adaptively select, allocate, compress, and evict key-value (KV) cache entries within transformer-based models to optimize inference under diverse resource, latency, and accuracy constraints. Rather than statically partitioning cache budgets or applying uniform compression, dynamic KV-cache management leverages live model signals such as attention distributions, token importance, activation uncertainty, and workload behavior to make near-optimal per-layer, per-head, or per-request decisions. This discipline underpins modern long-context LLM serving for scenarios ranging from single-GPU deployment to multi-machine retrieval, Mixture-of-Experts models, and database-scale cache stores.

1. Principles of Dynamic KV-Cache Management

The foundational motivation behind dynamic management is the highly unequal contribution of tokens, layers, or heads to final model output, and the rapid escalation of cache memory with long contexts or request batching (Wang et al., 7 Apr 2024, Zhou et al., 19 Dec 2024, Cai et al., 4 Jun 2024). In decoder-only transformers, each input or generated token accumulates key (K) and value (V) state per layer and head. Naïvely retaining every (K,V) pair for all tokens and all layers can quickly exceed tens of gigabytes, far outpacing both model weights and commodity GPU capacity.

Dynamic strategies exploit varying notions of token or layer “importance” (e.g., cumulative attention, cosine similarity, entropy), observed pyramidal or wave-like attention flows, and input- or task-dependent activation to reallocate a fixed or adaptive budget at runtime. These methods contrast with fixed-window, top-k, or static sparsity baselines which treat all parts of the model equally and fail to adapt to workload heterogeneity, sequence type, or specific model architecture (Wang et al., 7 Apr 2024, Zhong et al., 12 Dec 2024, Zhou et al., 19 Dec 2024).

2. Layer-Wise and Sequence-Wise Budget Allocation

A key axis is joint layer-wise and sequence-wise budget control. SqueezeAttention introduced cluster-based grouping of self-attention layers by impact (using prefill cosine similarity before/after each layer), then dynamically reallocated slice-wise budgets proportional to layer group importance (Wang et al., 7 Apr 2024):

• Compute per-layer similarity scores $s_i$, partition by k-means, and assign minimum allowable budget to least important layers.
• Within each layer, employ any sequence-wise compression (sliding window, heavy-hitter).

PyramidKV formalized attention “funneling,” empirically showing that lower layers attend broadly, while higher layers concentrate on sparse “sinks.” It then allocated more cache to lower layers, less to upper, using simple arithmetic decay controlled by hyperparameters $\alpha$ and $\beta$ (Cai et al., 4 Jun 2024).

DynamicKV extended this by periodically recalibrating per-layer budgets based on pooled activation statistics, enabling adaptation to input and task type (summarization pyramids, code “waves,” QA mid-level focus) (Zhou et al., 19 Dec 2024).

ZigZagKV measured per-layer attention and hidden-state uncertainty (e.g., minimum budget for 90% attention mass) to allocate budget proportionally, with a lower bound (Zhong et al., 12 Dec 2024).

Method	Layer Grouping	Budget Criterion	Application Mode
SqueezeAttention	k-means	Cosine similarity before/after attn	Prefill & decode
PyramidKV	arithmetic	Layer-wise attention entropy/top-k share	Fixed pyramidal
DynamicKV	live pooling	Task-adaptive activation patterns	Periodic inference
ZigZagKV	calibration	Layer attention mass uncertainty	Online compression

This multi-dimensional budget control yields 30–70% memory reduction and up to 2.2× throughput gain at near-iso-accuracy (Wang et al., 7 Apr 2024, Cai et al., 4 Jun 2024), with dynamic/tailored allocation outperforming static counterparts under tight compression.

3. Token Importance, Eviction, and Compression

Widely adopted importance metrics include:

• Per-token cumulative attention (heavy-hitter, H2O, SnapKV).
• Window-based recent attention.
• Cosine similarity between query and key.
• Aggregated semantic scores (WindowKV, semantic-aware boundary detection in LouisKV (Wu et al., 13 Oct 2025)).
• Graph-based decay-signal propagation using token similarity (GraphKV: propagate penalties across high-cosine neighbors to balance diversity and importance) (Li et al., 30 Aug 2025).

DBudgetKV dispenses with pre-defined budgets, using an attention-norm based metric to greedily prune until the Frobenius norm drop exceeds a threshold (often set at 1%)—delivering near-lossless performance for lossless, input-adaptive compression (Ni et al., 24 Feb 2025).

For hardware efficiency, KVComp combines blockwise quantization with Huffman encoding to compress cache tensors, maintaining attention accuracy and boosting kernel throughput—even outperforming cuBLAS mat-vec for long contexts (Jiang et al., 30 Aug 2025).

LeanKV exploits nonuniformity across keys and values (keys dominate $\alpha_{ij}$ contribution; values have smaller dynamic range), token importance, and head-wise sparsity. The memory manager dynamically partitions precision levels and manages coalesced page allocation on-GPU (Zhang et al., 4 Dec 2024).

4. System-Level and Multi-Tenant Management

On the system scale, dynamic KV-cache management spans:

• Multi-GPU and multi-node expert sharding for Mixture-of-Experts (PiKV): expert-sharded KV storage, sparse routing, adaptive compression, and eviction scheduling (Liu et al., 2 Aug 2025).
• Online GPU scheduling with adaptive migration to minimize GPU usage and balance cache load; Mell’s constant-migration-bounded algorithm achieves up to 31% hardware savings (Qianli et al., 12 Jan 2025).
• Database-inspired LSM-tree storage (SGLang-LSM): adaptive cost-model controlled compaction, prefix-preserving indexing, and batch-optimized read/write for disk-based KV layers (Yu et al., 20 Nov 2025).
• Semantic-aware batch retrieval and cache offload (LouisKV): trigger retrieval only at semantic boundaries, separating prompt clustering from output segmentation, yielding 4.7× speedup in long-sequence inference (Wu et al., 13 Oct 2025).

SCBench provides comprehensive lifecycle evaluation, finding that purely sub-$O(n)$ memory schemes collapse with single-token retention in multi-turn/shared-context settings, while dynamic sparsity (MInference) and hybrid layer-level designs preserve accuracy and expressive caches across shared and evolving requests (Li et al., 13 Dec 2024).

5. Unified Frameworks and Theory

CAKE formalizes budget allocation as an adaptive, preference-weighted “cake-slicing” problem, scoring each layer by spatial attention entropy and temporal variability, reassigning and evicting cache allocation in a cascaded manner during prefilling and decode (Qin et al., 16 Mar 2025). LAVa establishes a unified loss-based framework, computing the differential impact of cache eviction on layer-residual streams, and dynamically partitions both head- and layer-wise budgets to minimize final logit loss (Shen et al., 11 Sep 2025).

GraphKV generalizes selection by propagating context-dependent decay across a token similarity graph, adaptively balancing raw token importance with semantic coverage—improving strong methods like SnapKV and PyramidKV by 0.2–0.5 F1 on LongBench and ≥6% on Needle retrieval (Li et al., 30 Aug 2025).

The survey by Li et al. (Li et al., 27 Dec 2024) categorizes all major dynamic strategies into token-level selection, merging, quantization, model-level attention mechanisms, layer/head allocation, and system-level storage/scheduling. It reports 2–4× memory savings and up to 50% throughput improvement with sub-1% accuracy penalty, and tabulates comparative properties for production deployment.

6. Practical Results, Benchmarks, and Empirical Tradeoffs

Benchmark studies consistently demonstrate that dynamic, multi-dimensional KV-cache management outperforms static baselines, especially in long-context, multi-turn, and extreme compression scenarios:

• LongBench and Needle-in-a-Haystack: SqueezeAttention, PyramidKV, DynamicKV, ZigZagKV, WindowKV, LAVa, CAKE, LeanKV all report 85–100% relative accuracy at 12–20% cache retention; DBudgetKV achieves lossless performance at up to 36% pruning (Zhou et al., 19 Dec 2024, Cai et al., 4 Jun 2024, Zhong et al., 12 Dec 2024, Ni et al., 24 Feb 2025, Zuo et al., 23 Mar 2025).
• Throughput gains: up to 2.2× with SqueezeAttention, 1.9–5.4× with LeanKV (Wang et al., 7 Apr 2024, Zhang et al., 4 Dec 2024).
• GPU utilization: Mell sustains 88–95% utilization, 10–43 points higher than baseline (Qianli et al., 12 Jan 2025).
• Speedup: LouisKV achieves up to 4.7× lower retrieval latency in long-sequence output tasks (Wu et al., 13 Oct 2025).
• Scalability: SGLang-LSM stores 50M entries, 143% higher cache hit, and 24% lower time-to-first-token under shifting load vs. file-object backends (Yu et al., 20 Nov 2025).

Method	Memory Reduction	Accuracy δ vs Full	Throughput Gain
SqueezeAttention	30–70%	<1pt drop	Up to 2.2×
PyramidKV	88% (12% left)	Matches FullKV	≈FullKV throughput
DynamicKV	98.3% (1.7% left)	~85%–100%	Up to 90% memory save
LeanKV	2.7–5.7×	≤FP16 σ (lossless)	1.9–5.4×
CAKE/LAVa	96.8% (3.2% left)	>90% in Needle	>10× latency speedup

7. Current Limitations, Best Practices, and Future Directions

Current limitations include calibration steps for layer uncertainty estimation (ZigZagKV), handling attention shift in rapidly changing input (InfiniGen), dynamic error budgeting per block (KVComp), and lack of theory bounding long-term loss under aggressive pruning. Extensions under discussion include learned cache policies, continual online compression, joint pruning–quantization–low-rank mechanisms, speculative prefetching, universal multimodal cache management, and privacy/fairness-aware eviction criteria (Li et al., 13 Dec 2024, Li et al., 27 Dec 2024).

Best practices documented in the literature:

• Utilize lightweight online scoring (attention, entropy, semantic boundaries) for real-time budget adaptation.
• Prefer joint layer–sequence strategies over single-dimensional pruning for large contexts.
• Use multi-tier memory and batch-prefetching for high-throughput in multi-tenant systems.
• Always calibrate hyperparameters (window size, budget ratio, error control) on held-out validation data before full deployment.

Dynamic KV-cache management is now a core ingredient in LLM serving frameworks, enabling efficient scaling across context lengths, request concurrency, user personalization, and hardware heterogeneity—all with provable or empirically verified accuracy and throughput guarantees (Wang et al., 7 Apr 2024, Cai et al., 4 Jun 2024, Zhou et al., 19 Dec 2024, Zhong et al., 12 Dec 2024, Ni et al., 24 Feb 2025, Wu et al., 13 Oct 2025, Zhang et al., 4 Dec 2024, Lee et al., 28 Jun 2024, Qianli et al., 12 Jan 2025, Qin et al., 16 Mar 2025, Shen et al., 11 Sep 2025, Yu et al., 20 Nov 2025, Jiang et al., 30 Aug 2025, Liu et al., 2 Aug 2025, Li et al., 27 Dec 2024).