KVCache-Centric Buffering

• KVCache-centric buffering is a set of techniques that manage key-value caches in transformer LLMs to optimize memory and computation.
• It employs methods such as split buffering, selective compression, and adaptive allocation to alleviate memory bottlenecks and bandwidth constraints.
• Recent approaches demonstrate significant efficiency gains, with improvements up to 60% memory reduction and 5.5x throughput acceleration.

A key-value cache ("KVCache") is a central data structure in modern transformer-based LLMs, enabling efficient autoregressive inference by storing intermediate key and value vectors representing processed tokens at each layer. While KVCache-centric buffering, which refers to all operational, architectural, and algorithmic strategies for managing, compressing, and allocating the KVCache, greatly accelerates generation, it also presents formidable memory and bandwidth constraints—especially as models process long contexts and serve high-throughput workloads. Recent research has focused not only on reducing raw memory requirements but also on sophisticated forms of allocation, adaptive replacement, and inter-stage cache movement. The sections below survey major algorithmic ideas, technical metrics, and practical trade-offs in KVCache-centric buffering.

1. Memory Bottlenecks and the Classical KVCache Paradigm

The classical approach in transformer inference stores both key (K) and value (V) tensors for every token, at every transformer layer, directly on high-bandwidth GPU memory (HBM). The storage requirement grows as:

$$\text{KV Cache Memory} = 2 \times \text{bytes} \times b \times s \times d \times l$$

where $b$ is batch size, $s$ is current sequence length, $d$ is hidden dimension, $l$ is number of layers, and the factor 2 accounts for both key and value tensors. For long text or large-batch applications this memory demand can surpass the storage required by model weights, bounding batch size and maximum context length, and thereby constraining achievable throughput and task scale.

Classical buffering involves unconditional retention of all K and V tensors, maximizing accuracy but rapidly saturating GPU memory. Consequently, a wide array of KVCache-centric buffering methods have emerged, focusing on decomposing, compressing, adaptively partitioning, or partially offloading KV states without inducing significant accuracy loss.

2. Split Buffering and Cross-Memory Offloading

One strategy is to split the KVCache based on tensor type or importance and selectively place subsets in heterogeneous memory regions. The KCache method (He et al., 28 Apr 2024) is prototypical, retaining the entire K tensor in HBM while offloading most V tensors to CPU memory during the prefill phase. In the decode phase, the attention calculation is modified: softmax-based attention is computed fully, but only the top-N attention-scored tokens and their associated V states (stored on CPU) are transferred into HBM on demand for final attention aggregation. If $N$ is chosen such that $s/N$ exceeds the ratio of GPU bandwidth to host-to-device (CPU-GPU) bandwidth, the transfer overhead is marginal, enabling substantial context size and throughput improvements (up to 40% tokens/s compared to baseline) without significant impact on accuracy.

This "selective pull" mechanism achieves:

• Substantially reduced HBM pressure, as only the K cache remains resident on the GPU, enabling longer sequence support and larger batch sizes.
• Minimal latency overhead provided $N$ is not set so low as to omit relevant attention mass.
• A parameterizable trade-off between memory usage and potential information loss, controlled by $N$ (top tokens) and $L$ (the number of layers whose V caches are always held in HBM).
• Direct applicability to pre-trained, decoder-only transformer models with no retraining.

Such split-buffering approaches demand careful orchestration of cross-memory asynchronous data transfers and can marginally complicate pipeline architecture, but they offer critical scalability for long-context or memory-bound deployments.

3. Selective Compression and Approximate Retrieval

Systematic reduction of KVCache storage without offloading is possible through adaptive token pruning and compression. Product Quantization-based approaches like PQCache (Zhang et al., 1 Jul 2024) treat the KV cache as a typical embedding retrieval problem. High-dimensional keys are partitioned into $m$ sub-vectors and quantized by KMeans clustering into lower-bit centroids, creating compact PQ codes for each token. At decode, attention relevance estimation is mapped onto a Maximum Inner Product Search (MIPS) problem, enabling fast, approximate retrieval of the $k$ most important KV pairs using only PQ codes and centroids.

PQCache integrates this with block-level caching, asynchronous fetches, and adaptive overlapping during both prefill and decode phases. It achieves:

• Compression of up to 80% of tokens in attention with negligible accuracy loss on diverse tasks.
• Low and amortized inference latency due to efficient PQ coding and retrieval.
• Robustness even when processing only one-fifth of context tokens for attention computation.

Other strategies such as KVCrush (Jha et al., 24 Feb 2025) use attention fingerprinting to transform high-dimensional KV tokens into binary "signatures" based on head-wise attention distributions, efficiently grouping tokens for aggressive but low-latency cache reduction.

4. Layer-wise and Head-wise Adaptive Buffering

Static, uniform allocation of cache space across all layers or heads is suboptimal. Recent approaches optimize cache allocation at finer granularity by:

• Profiling importance of each attention head or individual KV cache via token-wise cosine similarity between original V and post-attention outputs (BaKlaVa (Gulhan et al., 18 Feb 2025)) and dynamically distributing memory according to the observed "importance" ranking.
• Personalizing cache size per layer based on empirical or theoretical metrics such as the "importance retention ratio" (XKV (Li et al., 8 Dec 2024)). Here, the allocation problem is cast as a combinatorial optimization—maximizing average retention across layers under a fixed memory budget—using greedy algorithms proven to be globally optimal under the discrete token-allocation regime.
• Sharing KV caches across layers to reduce redundancy, e.g., KVSharer (Yang et al., 24 Oct 2024), which finds that sharing caches between dissimilar layers (as judged by Euclidean distance of average KV cache vectors) often preserves accuracy better than sharing between similar layers, a counterintuitive result confirmed by ablation studies.

These methods can yield over 60% average KV cache memory reduction with little or no loss in downstream task performance, with acceleration in inference throughput ranging from 1.3x up to 5.5x.

5. Lossy Quantization, Correction, and In-Cache Computation

Extreme quantization of the KV cache (e.g., to 2 bits per entry) is a critical target for buffering in long-context, large-scale scenarios. However, naive quantization induces significant errors in the attention mechanism. The KVLinC framework (Saxena et al., 6 Oct 2025) advances this by:

• Quantizing raw keys channel-wise and Hadamard-rotated values token-wise to balance outliers and reduce per-block scaling factors.
• Introducing lightweight linear correction adapters trained to compensate for error in quantized keys directly within the attention softmax, ensuring the resulting distribution closely matches the unquantized baseline.
• Fusing quantization, dequantization, and matrix-vector computation in a custom, high-throughput GPU kernel to stream KV blocks efficiently with minimal data movement.

This results in up to 2.55x runtime acceleration over FlashAttention, increases in batch-size support by up to 3.5x, and competitive or superior performance in perplexity and downstream reasoning/QA tasks compared to all strong baselines.

System–algorithm co-design is also central in frameworks like KVComp (Jiang et al., 30 Aug 2025), which applies two-stage buffering: blockwise channel quantization with shared GPU-optimized Huffman encoding during prefill, and cache-resident, just-in-time decompression fused with kernel computation at decode. At sequence lengths $s > 8$k, decomposition and memory fusion can even accelerate core computations over cuBLAS-based kernels due to reduced data movement, while maintaining accuracy within 3%.

6. Dynamic Budgeting, Workload-Aware Eviction, and Real-World Production

Production LLM serving systems impose further challenges: buffer capacity varies with workload, input type, and SLO targets, and naive sparsity or quantization strategies may degrade specific requests ("negative samples") or even increase end-to-end latency due to longer outputs or non-optimized system integration (Gao et al., 31 Mar 2025).

Dynamic methods like DBudgetKV (Ni et al., 24 Feb 2025) combine importance-based ranking (preserving initial tokens, then ranking others by attention score or position) with an attention-based stopping metric (Frobenius-norm difference between full and reduced attention matrices), halting pruning when quality loss is imminent. This enables per-layer and per-input dynamic budget scaling, achieving full-cache-level performance in most tested cases with only 63% average cache occupancy.

Cloud-scale characterization (Wang et al., 3 Jun 2025) reveals additional patterns: reuse probability of KV blocks follows an exponential law and is highly workload-dependent. A simple tuple-based policy coupling estimated block reuse probability (via fitted distributions) and spatial locality (token offset) enables near-ideal hit rates even at moderate buffer sizes. Rather than using traditional frequency-based eviction, these strategies rely on recency and per-category reuse fits, maximizing effective utilization of scarce GPU and CPU buffer regions. Experiments with real provider traces show 41% latency improvement over conventional policies.

7. Applicability, Trade-Offs, and Integration

The diversity of KVCache-centric buffering methods makes them suitable for a wide range of scenarios, from maximizing batch size on single-GPU machines to supporting ultra-long sequence reasoning in distributed or resource-constrained deployments. Broadly:

• Split/memory-tiering approaches (e.g., KCache, PQCache) are most useful when host memory is plentiful and low cross-device bandwidth is a bottleneck.
• Lossy compression and quantization approaches (KVCrush, BaKlaVa, KVLinC, KVComp) are best for deployments where reducing buffer size is paramount and some level of approximate attention can be tolerated.
• Layer/head-specific or dynamic strategies (XKV, DBudgetKV, KVSharer, SpindleKV, KVCompose) adapt to structure and task, optimizing memory in response to empirical importance or workload characteristics.
• New techniques such as composite tokens [KVCompose (Akulov et al., 5 Sep 2025)], tree-structured smoothing [TreeKV (He et al., 9 Jan 2025)], or cross-prompt semantic cache reuse [SemShareKV (Zhao et al., 29 Sep 2025)] are expanding the boundaries of what constitutes efficient KV buffering, with direct compatibility with production inference engines and support for advanced serving logic.
• Infrastructure-specific offloads (e.g., FlexiNS SmartNIC stack (Chen et al., 25 Apr 2025)) show that KVCache movement, signaling, and transfer itself can become the dominant bottleneck, requiring hardware–software co-optimization not just at the algorithmic but at the transport level.

Limitations and open challenges persist: asynchronous CPU-GPU data transfer introduces pipeline complexity; fixed budget heuristics may fail under input or workload shift; aggressive quantization risks sharp degradation beyond a quality "cliff"; and methods often trade latency, accuracy, and throughput along hardware- and application-specific axes.

Conclusion

KVCache-centric buffering encapsulates a rapidly evolving spectrum of techniques for memory- and compute-efficient inference with transformer LLMs. The paradigm has shifted from naïve full-state retention to a sophisticated toolkit of selective memory region assignment, importance-driven dynamic allocation, lossy compression with explicit error management, and real-time, hierarchical eviction strategies. The diversity and composability of these methods allow practitioners to tailor buffering to the structural, operational, and economic demands of their systems—balancing memory, throughput, and accuracy to enable new levels of model utility and scalability. Continued research, especially at the intersection of algorithmic and system design, is poised to further expand the efficiency and flexibility of KVCache-centric buffering in future LLM deployments.