KVCache-Centric Buffering

Updated 23 November 2025

KVCache-Centric Buffering is an advanced set of methods for managing, compressing, and sharing key–value caches in large language models, addressing memory growth and inefficient data movements during long-context inference.
It employs intra-query, cross-query, and hybrid offload architectures that strategically prune, quantize, and share cache slices to optimize GPU utilization and reduce processing overhead.
Empirical results demonstrate significant improvements such as up to 6.25× speedup, 61% memory savings, and minimal quality loss, while also highlighting practical challenges in hyperparameter tuning and system integration.

KVCache-centric buffering encompasses a collection of advanced algorithms and system designs for managing, compressing, offloading, and sharing the key–value (KV) caches produced during inference in LLMs. As attention cache memory footprints grow rapidly with context length and model depth, KVCache-centric buffering aims to support long-context inference and high-throughput serving by minimizing redundant storage and computation, orchestrating efficient memory hierarchies, and intelligently compressing or evicting less critical KV entries. Methods in this space leverage token-level, head-level, layer-wise, temporal, semantic, and workload-adaptive strategies, with empirical results showing substantial gains in throughput, hardware utilization, and memory reduction while preserving generation quality.

1. Motivations and Core Challenges

The structure of transformer-based LLMs necessitates caching all past key and value projections for each layer and attention head, resulting in linear or superlinear scaling of memory with sequence length, batch size, and model width. With modern architectures—e.g., sequences exceeding 128k tokens and models with dozens of layers and large embedding sizes—the KV cache rapidly dominates available GPU memory, restricting both throughput and achievable context lengths. Challenges extend beyond memory: inefficient cache access patterns induce excessive data movement, PCIe/NVLink bandwidth bottlenecks, and elevated prefill latency, all hindering scaling in both single- and multi-query workloads (Zhao et al., 29 Sep 2025, Li et al., 2024, Yi et al., 18 Nov 2025).

Further complications arise from lexical and semantic diversity across prompts and tasks, making naïve cache reuse or static compression suboptimal. The central challenge is to exploit the structured redundancy in attention, user access, and model behavior to buffer, share, compress, and transfer just the essential KV cache slices, adapting dynamically to input, task, and hardware constraints.

2. Architectural Patterns and Buffering Workflows

The architectural paradigms for KVCache-centric buffering fall into three primary classes:

Intra-query compression/reuse: Compressing or pruning tokens within a single prompt by attention or importance; sharing or reordering cache entries among highly similar (but not strictly prefix-identical) prompts (Zhao et al., 29 Sep 2025, Li et al., 2024, Ni et al., 24 Feb 2025).
Cross-query and cross-engine sharing: Sharing cache slices between queries or across inference engines through hierarchical or distributed buffering layers, commonly relying on modular interfaces and chunked data movements (Cheng et al., 8 Oct 2025, Wang et al., 3 Jun 2025).
Hybrid offload and quantization: Orchestrating CPU–GPU memory management, quantization, and dynamic token/head selection for hardware-efficient execution (Yao et al., 26 May 2025, Yi et al., 18 Nov 2025).

A canonical buffering loop first materializes full or partial KV caches during the prefill (context) phase. Selective pruning, quantization, offloading, or reordering is then applied, either immediately or post-prefill. The decode phase proceeds with highly compressed or windowed KV buffers—pruning unimportant tokens, packing representations, or transferring only top-k slices on demand. These architectures are implemented with GPU and CPU co-designs, attention-aware eviction protocols, prefetch streams, and cross-engine control interfaces (Zhao et al., 29 Sep 2025, Cheng et al., 8 Oct 2025, Yi et al., 18 Nov 2025).

3.1 Token and Layer-wise Pruning

Strategies such as XKV and DBudgetKV perform differential importance estimation per layer and token, using either attention-weight statistics or cumulative relevance metrics (Li et al., 2024, Ni et al., 24 Feb 2025). Tokens are pruned layer-wise by maximizing the importance-retention utility given a hard or dynamic cache budget.

XKV solves a greedy-optimal combinatorial allocation of per-layer cache sizes, leveraging layer-specific importance profiles built via lightweight mini-prefill runs. The allocation minimizes overall cache usage at a fixed accuracy or maximizes accuracy within a memory budget, achieving over 60% memory reduction and 2.1× computational efficiency (Li et al., 2024).
DBudgetKV introduces an attention-mass thresholding scheme, pruning tokens only until a Frobenius-norm criterion signals possible degradation, thus achieving inherently lossless compression—often >36% cache reduction with <1% quality drop (Ni et al., 24 Feb 2025).

3.2 Semantic and Fuzzy Prefix Reuse

SemShareKV generalizes prefix reuse to semantically similar but lexically distinct prompts using token-level Locality-Sensitive Hashing (LSH) on embedding spaces and rotary position embedding (RoPE) integration for positional sensitivity (Zhao et al., 29 Sep 2025). The workflow constructs a provisional mapping between the target and reference prompt tokens, reorders and selectively recomputes KV entries, and prunes or evicts low-attention tokens at each layer, yielding up to 6.25× speedup and 42% GPU memory savings.

3.3 Quantization and Hybrid Compression

Hybrid approaches such as MiniKV and TailorKV apply mixed granularity quantization: aggressively quantizing “dense” or global layers (down to 1 or 2 bits per entry), while retaining higher precision, dynamic top-k offloading, or selective channel fetch in “sparse” or attention-focused layers (Sharma et al., 2024, Yao et al., 26 May 2025). Full pipeline orchestration is designed for overlapping PCIe offloads, CUDA-fused dequantization, and minimal accuracy loss (<1.5%) at extreme compression rates (up to ×32 reduction).

KVSharer enables dissimilarity-based layer-wise cache sharing, copying KV entries not from the most statistically similar but from maximally different layers, empirically found to preserve downstream performance better—achieving ∼30% memory saving with just 2–3% degradation at moderate sharing ratios (Yang et al., 2024). CLO applies head-wise approximate caching with per-head coarse matching and dynamic thresholds, integrating this with persistent on-GPU buffering and prefetching for throughput-optimal offloading (Yi et al., 18 Nov 2025).

3.5 Cascaded and Scheduled Token Retention

Training-free buffer scheduling schemes, such as Cascading KVCache, use multi-level sub-caches that retain recently used or highly attended tokens and sample older tokens at an exponentially decimated rate—thereby extending the context span exponentially without increasing total memory. These systems implement both strided prompt prefill and exponential cache retention, achieving linear time/memory and significant empirical throughput gains (Willette et al., 2024).

4. System Integration and Distributed Serving

Enterprise-scale serving architectures (e.g., LMCache, CLO) deploy KVCache-centric buffering as a first-class data movement and storage primitive (Cheng et al., 8 Oct 2025, Yi et al., 18 Nov 2025). LMCache inserts a dedicated buffer layer between inference engines and the memory/storage/network hierarchy, exposing APIs for cache pinning, lookup, offload, transfer, and lossless/compressed management. Asynchronous pipelining, reference-counted chunk transfers, connector APIs abstracted from engine specifics, and hierarchical cache layering are central.

Workload-aware buffer allocation and eviction policies benefit from temporal and frequency statistics of real traces. For instance, KVCache Cache in the Wild demonstrates that per-category, exponential-fit cache hit models enable eviction protocols targeting maximal future reuse yield, improving both hit ratios and end-to-end throughput under limited memory (Wang et al., 3 Jun 2025).

The combination of CPU–GPU offload, persistently resident hot caches, zero-copy data movement (e.g., via GDRCopy and custom AVX-accelerated kernels), and distributed controller–worker design in architectures such as CLO and LMCache result in order-of-magnitude improvements in throughput and system utilization (Yi et al., 18 Nov 2025, Cheng et al., 8 Oct 2025).

5. Empirical Impact and Performance Analysis

The impact of these buffering strategies has been consistently reported across major classes of models and tasks:

Buffering Method	Compression Ratio	Speedup/Throughput	Quality Loss	Reference
SemShareKV (LSH+RoPE)	42% memory saved	Up to 6.25× TTFT	ΔROUGE-L < 1.0	(Zhao et al., 29 Sep 2025)
XKV (layer-adaptive)	61.6% saved	2.1× compute; 5.5× batch	< 0.1%	(Li et al., 2024)
DBudgetKV (dynamic)	36.3% (avg)	Up to 10% faster overall	≤1% (>21/39 matched/exceed)	(Ni et al., 24 Feb 2025)
PureKV (video VLLM)	5× cache comp.	3.16× prefill; 1.3× decode	–4.46pp (20% budget)	(Jiang et al., 29 Oct 2025)
VL-Cache (mod.-aware)	90% memory saved	2.33–7.08× decode speedup	<2%	(Tu et al., 2024)
LMCache (distributed)	–	Up to 15× throughput	–	(Cheng et al., 8 Oct 2025)
CLO (co-design offload)	–	+9.3%–66.6% throughput	Equivalent full-cache	(Yi et al., 18 Nov 2025)
Cascading KVCache	–	6.8× prefill speedup@1Mtok	+12% (QA), +4% (summarize)	(Willette et al., 2024)

These methods consistently enable throughput and memory usage improvements at near-baseline generation quality in both language and vision-language domains, sometimes even improving specific metrics due to noise reduction or cache purification.

6. Limitations, Challenges, and Best Practices

Key limitations and practical considerations are identified:

Overhead on very short contexts: For prompts shorter than 700 tokens, the cost of LSH, attention-guided pruning, or rearrangement may dominate (Zhao et al., 29 Sep 2025).
Hyperparameter sensitivity: Many methods depend on attention thresholds, budget fractions, number of tokens or heads to retain, and LSH banding; these require validation per model size (Li et al., 2024, Ni et al., 24 Feb 2025).
Compatibility with fast attention kernels: Some techniques (e.g., PureKV) are specifically designed for compatibility with FlashAttention or block-sparse CUDA libraries, ensuring downstream efficiency (Jiang et al., 29 Oct 2025, Sharma et al., 2024).
Extensibility to other modalities or architectures: While text-centric, modalities such as vision and audio require modality-aware sparsity and attention estimation strategies (e.g., spatial–temporal in video) (Jiang et al., 29 Oct 2025, Tu et al., 2024).
System-level integration and control: Fully leveraging distributed buffering requires connector APIs, failure-tolerant cache control, and workload trace analysis as evidenced by LMCache's adoption lessons (Cheng et al., 8 Oct 2025).
Compound strategies: Layer-wise methods (e.g., KVSharer) and intra-layer methods (pruning/quantization) can be combined for superlinear memory savings (Yang et al., 2024).

Best practices include prompt-adaptive budget allocation, persistent pinning of critical contexts, hardware-specific tuning, and, where possible, decoupling model logic from cache layout via standardized interfaces. Lossy quantization may be embraced in non-critical applications to further reduce bandwidth without measurable impact (Cheng et al., 8 Oct 2025, Yao et al., 26 May 2025).

7. Outlook and Future Directions

Directions for advancement include further semantic and adaptive matching for prompt reuse, attention-guided dynamic thresholds, fine-tuning for application-specific compression–quality tradeoffs, and broader adoption of cache-centric management at the system orchestration layer. The LSH+selective-recompute paradigm of SemShareKV and cross-layer importance propagation of PureKV point to broader unification between algorithmic and systems-level approaches to buffering. The confluence of workload-aware prioritization, quantization, offloading, and distributed cache orchestration will be central to supporting the next generation of million-token, multi-modal models (Zhao et al., 29 Sep 2025, Jiang et al., 29 Oct 2025, Cheng et al., 8 Oct 2025).

References: (Zhao et al., 29 Sep 2025, Li et al., 2024, Ni et al., 24 Feb 2025, Jiang et al., 29 Oct 2025, Tu et al., 2024, Yi et al., 18 Nov 2025, Cheng et al., 8 Oct 2025, Wang et al., 3 Jun 2025, Yao et al., 26 May 2025, Sharma et al., 2024, Yang et al., 2024, Willette et al., 2024)