Tensor-Centric KV Cache Transfer Protocol

• The protocol offers an effective framework that organizes, compresses, and transfers multi-dimensional KV caches in transformer-based LLMs.
• It optimizes data layouts using block partitioning, quantization, and fused tensor operations to reduce latency and bandwidth usage.
• Empirical evaluations show significant throughput gains and reduced transfer times in multi-GPU and distributed environments.

A tensor-centric KV cache transfer protocol constitutes the set of architectural principles and mechanisms for efficiently organizing, compressing, transferring, and reconstructing key-value (KV) caches in transformer-based LLM systems, with a design tightly coupled to the structure of the underlying tensor computations. These protocols expressly target high-throughput, low-latency operations in both single- and multi-GPU, heterogeneous, and distributed environments, optimizing the lifecycle of the KV cache for scalable inference, context reuse, multi-agent systems, and inter-model communication.

1. Foundations and Motivations

The exponential growth in context window length, model size, and deployment scale in LLMs makes the KV cache—structured as high-rank, multi-dimensional tensors (e.g., $(L, H, S, D)$ for $L$ layers, $H$ heads, $S$ sequence length, $D$ feature dimension)—the central memory and communication bottleneck for both throughput and end-to-end latency. Tensor-centric transfer protocols address:

• The need for memory- and bandwidth-efficient data movement across GPUs or network links, leveraging quantization and compression.
• The imperative for formats and pipelines compatible with accelerator hardware primitives (e.g., Tensor Cores, NCCL).
• The facilitation of advanced use cases, such as disaggregated inference, multi-agent prompt reuse, and semantic inter-LLM communication, which require cache exchange beyond simple byte continuations.
• The requirement to adaptively allocate, compress, or share cache segments across attention heads, layers, or agents while maintaining computational balance and quality (Du et al., 24 Mar 2025, Li et al., 3 Apr 2025, Zhao et al., 19 Feb 2025, Fu et al., 3 Oct 2025, Ye et al., 14 Oct 2025, Lin et al., 16 Oct 2024, Liu et al., 2023).

2. Core Data Layouts and Memory Organization

A hallmark of tensor-centric protocols is the optimization of KV cache data layout for both logical access patterns and physical transfer efficiency. Key aspects include:

• Block Partitioning and Block-wise Fusion: Protocols such as FlowKV and BitDecoding partition the cache into contiguous "blocks" of tokens and fuse layer, K/V, and warps into transposed tensors of shape $(n, L, 2, b, H)$. This reduces the number of data segments to be scheduled for transfer or computation, aligning them for batched, large-granularity requests (Li et al., 3 Apr 2025, Du et al., 24 Mar 2025).
• Packing and Quantization for Hardware Primitives: Low-bit representations (e.g., 2-bit/4-bit quantized packs in BitDecoding) are tightly packed into registers as 16×16 or 16×8 tiles compatible with Tensor Core MMA (matrix multiply accumulate) instructions. BitFusion fuses quantization scales ($\mathrm{scale}$) and zeros ($\mathrm{zeropoint}$) with the data payload to minimize transfer and decode steps (Du et al., 24 Mar 2025).
• Segmented Allocation and Memory Pooling: Segment-based allocators (FlowKV) coalesce contiguous blocks within a small set of memory regions, further minimizing the number of discrete transfers and maximizing the probability that cross-node cache sharing incurs a single data movement (Li et al., 3 Apr 2025).
• Feature-axis Compression: MatryoshkaKV projects cache feature dimensions to lower-rank subspaces using trainable orthogonal matrices, yielding tensors of shape $(L, H, S, d_{l,h})$ for per-layer, per-head bottlenecks (Lin et al., 16 Oct 2024).

3. Transmission, Streaming, and Communication Protocols

Tensor-centric protocols design network and inter-GPU communication mechanisms that match the internal organization of the KV tensor:

• Single-call Bulk NCCL Transfer: Layer- and block-aligned fusion allows FlowKV to reduce cross-GPU transfers from $O(Ln)$ to $O(1)$ per request, for one or two large NCCL send/recv calls rather than per-layer or per-head granularities (Li et al., 3 Apr 2025).
• Streaming and Pipelining: Asynchronous, fine-grained pipelines (BitDecoding) exploit overlapping computation and data movement via CUDA streams, group-committed loads, and warp-level parallelism. CacheGen streams adaptively compressed bitstreams chunkwise, pipelining transfer and decode to minimize end-to-first-token (TTFT) latency (Du et al., 24 Mar 2025, Liu et al., 2023).
• Hierarchical Compression and Adaptation: CacheGen dynamically chooses the quantization/bitstream level for each chunk, using bandwidth estimation and scheduling logic to balance quality and SLO compliance (Liu et al., 2023).
• Semantic and Structural Alignment: Protocols like C2C design neural cache mapping networks for transferring higher-level semantics between LLMs, while KVCOMM introduces offset-corrected interpolation using anchor pools for multi-agent cache reuse across diverging prefixes (Fu et al., 3 Oct 2025, Ye et al., 14 Oct 2025).

4. Compression, Quantization, and Projection Mechanisms

Optimal use of communication and memory bandwidth requires aggressive cache compression with negligible quality loss:

• Bit-Level Quantization: BitDecoding quantizes FP16 caches to 2-bit or 4-bit using per-block min/max scaling and packs quantized values for batch transfer and compute, achieving $4\times$–$8\times$ compression in memory and bandwidth (Du et al., 24 Mar 2025).
• Trainable Low-Rank Projection: MatryoshkaKV applies orthogonal, learnable projections, trained jointly under a Matryoshka nested-rank scheme, optimally allocating per-head, per-layer ranks to fit a global budget with minimal prediction discrepancy (Lin et al., 16 Oct 2024).
• Adaptive, Chunkwise Tensor Encoding: CacheGen combines local delta transforms, layer-group vector quantization, and per-channel arithmetic coding to exploit KV tensor distributions and compress further than simple uniform quantization (Liu et al., 2023).
• Semantic Fusion and Gating: C2C integrates KV tensors from different models via learned neural MLP projections and head-wise gating, rather than elementwise or featurewise compression (Fu et al., 3 Oct 2025).

5. Scheduling, Load Balancing, and Multi-Resource Coordination

Scheduling is tightly bound to data layout and transfer in distributed environments:

• Load-Aware Scheduling: FlowKV defines per-node load scores combining queue, utilization, and token/window metrics, dynamically assigning prefill and decode roles and orchestrating cache movement to minimize stragglers and leverage memory layout for cache prefix hits (Li et al., 3 Apr 2025).
• Fair, Per-Head Replication: FairKV statically identifies memory-intensive heads, replicating them across underutilized GPUs with minimal additional communication (one NCCL broadcast per heavy head per token), balancing per-device memory and compute and achieving up to 1.66× throughput gains over TP-only assignment (Zhao et al., 19 Feb 2025).
• Streaming and Pipelining Across Resources: Pipelined design in CacheGen and BitDecoding overlaps CPU/GPU decode, transfer, and application-layer scheduling, enabling multi-tensor chunks to be processed in parallel and streamed efficiently (Liu et al., 2023, Du et al., 24 Mar 2025).

6. Protocol Extensions: Multi-Agent and Inter-Model Communication

Tensor-centric transfer protocols have evolved beyond single-model, single-GPU scenarios to enable advanced features:

• Direct Semantic KV Cache Exchange: C2C enables direct, non-textual communication between LLMs, employing neural projection and gating to transfer tensor representations at each layer, achieving substantial accuracy and latency improvements over token-based communication (Fu et al., 3 Oct 2025).
• Cross-Agent Offset Correction and Interpolation: KVCOMM enables reuse of previously computed KV-cache fragments by aligning positions (RoPE-based) and interpolating context-dependent offsets stored in online anchor pools, allowing 70–95% cache reuse and up to 7.8× TTFT reduction in multi-agent tasks with context divergence (Ye et al., 14 Oct 2025).
• Anchor-Pool Based Cache Reuse: Efficient matching of anchor embeddings and context offsets supports dynamic adaptation to distinct context arrangements and enables prompt parts or code snippets to be reused in collaborative or retrieval-augmented settings (Ye et al., 14 Oct 2025).

7. Empirical Performance and Impact

Tensor-centric KV cache transfer protocols deliver measurable improvements in memory, throughput, and latency:

Protocol	Compression/Speedup	Key Metrics (summarized)
BitDecoding	4× (4-bit), 8× (2-bit)	7.5× speedup (4090), 4.8× (A100), 8.9× (H100); 3×-4× lower decode latency (Du et al., 24 Mar 2025)
FlowKV	96% lower transfer latency	E2E throughput (LongBench): +15.2–48.9%; TTFT drop 0.944s→0.053s (Li et al., 3 Apr 2025)
FairKV	1.66× ↑ in throughput	Smoother per-GPU load with minimal traffic; hybrid TP+DP for head replication (Zhao et al., 19 Feb 2025)
MatryoshkaKV	60–75% compression rate	>90% accuracy retained at 60% compression; <10% perf. drop to 75% budget (Lin et al., 16 Oct 2024)
CacheGen	3.5–4.3× smaller cache	3.1–4.7× TTFT speedup over baseline; <2% drop in accuracy (Liu et al., 2023)
C2C (inter-LLM)	8.5–10.5% higher accuracy	2× speedup vs. text comm.; neural fusion outperforms token pipeline (Fu et al., 3 Oct 2025)
KVCOMM	7.8× TTFT reduction	70–95% cache reuse in multi-agent LLM; up to 430ms→55ms TTFT (Ye et al., 14 Oct 2025)

These protocols have become integral to state-of-the-art efficient LLM serving, hybrid and distributed system design, and the emerging paradigm of semantic tensor communication between LLM instances. They enable predictable trade-offs between accuracy, bandwidth, and latency under operational constraints, and allow seamless integration with varying cache shapes, quantization budgets, and scheduling backends.