Intermediate, Prefix & Multilevel Caching Schemes

• Intermediate, prefix, and multilevel caching schemes are methods that reduce recomputation by sharing and reusing partial results at strategic boundaries in high-throughput systems.
• They employ adaptive algorithms—such as LRU variants, content-addressed hashing, and tiered hierarchies—to optimize cache hit ratios, latency, and throughput in practical deployments.
• These approaches enable scalable inference and efficient data management, with implications for semantic tagging, hardware co-design, and future system architectures.

Intermediate, prefix, and multilevel caching schemes are a family of methods for reducing recomputation by sharing and reusing partial results at strategic boundaries. These approaches span foundational work in memory and distributed storage systems, but have reached new prominence in large-scale machine learning inference and modern pipeline execution, where high-dimensional state (e.g., key-value tensors for LLM attention, or results of multi-stage IR operators) must be accessed and managed under strict latency and memory constraints. This article surveys and synthesizes the formal definitions, algorithmic principles, system-level architectures, and empirical outcomes associated with these caching paradigms, focusing on the technical distinctions between prefix, intermediate/content-based, and multi-tiered (multilevel) approaches across contemporary domains.

1. Definitions and Canonical Schemes

Prefix caching refers to storing and retrieving the results (often key-value states or output vectors) corresponding to initial segments (prefixes) of an input or token sequence, enabling recomputation to be skipped whenever an input shares an exact prefix with previously processed data. This scheme underlies classical LRU-based caches, radix tries, and paged prefix-key maps as implemented in LLM serving systems (e.g., vLLM, SGLang) and multimedia servers for video-on-demand (Jayarekha et al., 2010).

Intermediate (content- or position-independent) caching generalizes this to allow caching and reusing arbitrary internal chunks or semantically identified blocks, not just strict prefixes. These caches exploit the fact that identical or near-identical substructures may occur at different positions or contexts (e.g., agentic LLM turns, item descriptors in recommendations, or CDC-chunked file segments). PIC systems store each chunk by content hash and, with appropriate position-correction, supply the corresponding partial results regardless of sequence location (Ma et al., 7 May 2026, Zhao et al., 8 May 2026).

Multilevel caching encompasses hierarchies or pools of caches (e.g., L0-L1-L2 in models, or per-GPU and cluster-global segment pools), as well as hybrid architectures combining prefix and intermediate caches. Multilevel designs optimize for hit rate and memory efficiency by arranging different policies or representations at each tier and coordinating admission, eviction, and lookup logic across them (Pan et al., 2024, Wu et al., 24 Aug 2025, Fang et al., 12 May 2026).

2. Algorithmic Principles and Policy Design

Prefix Caching Algorithms

Standard prefix schemes map input prefixes to cached results and employ LRU/LFU policies or variants thereof. In multimedia and LLM serving settings, system designers have evolved these into adaptive dynamic replacement (ADR) policies that balance recency and frequency using ghost lists (B1, B2) and self-tuning split points between tracks for recency (L1) and frequency (L2). The result is resilience to workload shifts and improved hit-rate and wait-time (Jayarekha et al., 2010).

Let $H$ denote the hit ratio and $U$ bandwidth utilization: $$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$ For LLM KV-caches, the basic lookup is radix-trie based: find the longest stored prefix sequence match, retrieve its full KV tensor, and recompute only the tail. These mechanisms are foundational in PagedAttention and RadixAttention implementations (Ma et al., 7 May 2026).

Intermediate/Content-Addressed Caching

Position-independent or intermediate caching stores segments by content (typically using chunking via rolling or Gear hashes) and corrects for sequence position. In MLA-based architectures, the KV row is factorized into a position-independent component $c_{KV}$ and a small-dimension RoPE vector $k_r$; position correction at lookup is reduced to a closed-form $\delta$-rotation on $k_r$: $$k_r^{(p)} = R(\delta)\,k_r^{(p_\text{src})}, \quad \delta = p-p_\text{src}$$ where $R(\delta)$ applies pairwise sinusoidal rotations per RoPE frequency (Ma et al., 7 May 2026).

Intermediate block caching in pipelines (e.g., RcLLM's item- and user-block caches) uses content hashes and block-aligned precomputed keys. For each block, matches trigger zero-copy reuse and position adjustment, with fallbacks to partial recompute (including selective attention correction in boundary overlaps) (Zhao et al., 8 May 2026).

Multilevel Cache Hierarchies

Multilevel schemes integrate prefix and content-based tiers, and often interface with L0/L1/L2 layer architectures (immediate, GPU, and long-term storage). Policies include (i) radix-prefix as the first fallback, (ii) content-addressed segment caches for chunks, and (iii) capacity- or compute-aware admission/eviction driven by utility scores: $$U_{\text{adm}} = P_{\text{hit}} \cdot S_{\text{compute}} - \lambda M_{\text{mem}}$$

$U$0

where $U$1 is a hit-probability forecast, $U$2 expected compute savings, $U$3 memory cost; $U$4 tunes recency vs. efficiency (Pan et al., 2024).

3. System-Level Architectures and Implementations

Prefix and Intermediate Caching in LLMs

Modern LLM serving systems implement per-GPU prefix caches with radix or paged memory structures. For distributed or multi-GPU pools, advanced resource-efficient KV-cache fetching is realized, for example, with SmartNIC offload—eliminating decompression interference and achieving pipeline overlap across network, decompression, and DMA (see ShadowServe). Chunking and minimal-copy buffer management further enable high-throughput cache transfer and scatter-gather for multi-chunk reuse (Xiang et al., 21 Sep 2025).

Multilevel and Pooled Cache Management

Segment-level pooling, as in TokenLake, fully decouples cache management from request scheduling. TokenLake exposes a declarative interface that organizes KV cache segments into a pool with metadata on access frequency, instance hosts, and deduplication. Heavy-hitter segments are flexibly replicated across overloaded instances, while less active segments are uniformly hashed. Deduplication and defragmentation are enabled by content-hash-indexed segment tables and a background compaction process (Wu et al., 24 Aug 2025).

At the pipeline level (e.g., IR pipelines in PyTerrier), implicit caching of common pipeline prefixes is performed by LCP (Longest Common Prefix) detection, while explicit operator-level or sub-pipeline caches (KeyValueCache, ScorerCache, RetrieverCache, IndexerCache) implement fine-grained lookup and reuse. These approaches are composable and can be combined for maximal redundancy elimination (MacAvaney et al., 14 Apr 2025).

Distributed and Stratified Caching

RcLLM exemplifies stratified multilevel caching for domain-structured LLM applications. Compact semantic-history caches are fully replicated for instant lookup, while massive item caches are sharded via affinity-based placement and replicated for hot items. Cache-aware global schedulers route requests to maximize immediate data locality and concurrency (Zhao et al., 8 May 2026).

4. Policy Innovations and Adaptive Techniques

Modern cache management has shifted from uniform LRU or LFU to semantic-, compute-, and workload-adaptive policies. SAECache routes KV blocks to semantic queues (e.g., chat, agentic, structural, decode) and learns per-token-type weights reflecting empirical reuse frequencies (system prompts, chain-of-thought, user, etc.). Global eviction scores integrate queue performance, semantic weights, and recency: $U$5 where $U$6 models block survival probability via log-normal or position-decay fits, and the policy is adapted online via feedback from miss-after-eviction rates and sliding-window statistics (Fang et al., 12 May 2026).

In hybrid LLMs, Marconi forecasts per-prefix reuse frequency and compute utility for selective admission; eviction combines LRU and FLOP-efficiency, grid-searching for optimal recency/effectiveness balance over realistic sessions. This paradigm is generalizable to production-level, multilevel cache architectures (Pan et al., 2024).

5. Theoretical Analysis and Fundamental Limits

Information-theoretic analysis of multilevel caching in hierarchical networks, as formalized in two-layer decentralized schemes, optimizes random placement and coded delivery across server-helper-user topologies. Rates $U$7 and $U$8 (server-helper and helper-user) are provably within multiplicative and additive bounds of the optimum: $U$9 where cross-layer caching gains are realized by omitting user-cached bits from helper transmissions, and hybrid storage splits optimize for system load (Zhang et al., 2016).

Simulation confirms substantial reduction in network load and higher resilience to workload spread as cache sizes increase; these results justify chunked and stratified schemes (segment-level, block-level, and multilevel designs) in both theoretical and practical deployments.

6. Empirical Outcomes and Performance Metrics

Across systems and domains, key metrics include cache hit ratio, time-to-first-token (TTFT), loaded time-per-output-token (TPOT), throughput, and prefill energy savings.

• ShadowServe achieves up to $$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$0 lower TPOT and $$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$1 lower TTFT compared to GPU-side decompression solutions under bandwidth constraints, translating to $$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$2 maximum throughput improvement (Xiang et al., 21 Sep 2025).
• Marconi reaches $$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$3 higher token hit rates and $$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$4 lower TTFT relative to standard fine-grained prefix caches in hybrid LLM serving (Pan et al., 2024).
• SAECache improves TTFT by $$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$5–$$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$6 over production-style LRU baselines, and its online adaptation mitigates workload-mismatch risks that degrade static-parameterized policies (Fang et al., 12 May 2026).
• Irminsul demonstrates up to $$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$7 additional token recovery above prefix-only caches in agentic workloads, saving $$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$8–$$H = \frac{\text{cache hits}}{\text{total requests}}, \quad U = \frac{\text{cache-served bytes}}{\text{total bytes served}}$$9 of prefill energy per cache hit and reducing TTFT by up to an order of magnitude (Ma et al., 7 May 2026).
• RcLLM delivers $c_{KV}$0–$c_{KV}$1 TTFT reduction over prefix-only methods in recommendation serving, while preserving near-original accuracy via selective correction (Zhao et al., 8 May 2026).
• TokenLake increases goodput by up to $c_{KV}$2 and hit rates by up to $c_{KV}$3 vs. cache-aware router and PD-disaggregation baselines (Wu et al., 24 Aug 2025).
• In VoD servers, ADR policies yield $c_{KV}$4 hit ratio (vs. $c_{KV}$5 for LRU) and halve average wait time (Jayarekha et al., 2010).

7. Broader Implications and Future Directions

These caching frameworks have enabled efficient scalable inference in LLM serving, federated IR pipelines, and large-scale video delivery, influencing both theoretical design (order-optimality and rate bounding) and practical systems (GPU, SmartNIC, memory, and network-aware architectures). Future desiderata include deeper integration of content-addressed schemes as primitives in serving stacks, extensions to chunked prefill and partial-hit models, and further hardware co-design (e.g., SmartNICs with more memory channels, on-path DPAs) to support higher-throughput, higher-granularity multilevel caching (Xiang et al., 21 Sep 2025, Wu et al., 24 Aug 2025, Ma et al., 7 May 2026). Discriminative, semantic, and workload-aware adaptation is expected to remain central as cacheable content and model capacity continue to scale.

A plausible implication is that as model and data heterogeneity increases, intermediate and multilevel caches—supported by fine-grained, content- and usage-adaptive admission/eviction—will supplant traditional prefix-based approaches, especially in agentic, compositional, and high-throughput settings. Advances in segment-based pooling, semantically tagged multi-queue retention, and cross-layer coordinated strategies are likely trajectories for ongoing research and system deployment.