KV Packet Enables Recomputation-Free Context-Independent Caching for LLMs
KV Packet eliminates recomputation for reusable KV caches via soft-token adapters, cutting FLOPs and TTFT while matching full-recompute accuracy.
KV Packet treats cached documents as immutable packets wrapped in lightweight trainable soft-token adapters trained via self-supervised distillation to bridge context discontinuities without any KV recomputation (Chen et al., arXiv:2604.13226). This yields near-zero additional FLOPs and lower Time-to-First-Token than selective recomputation baselines while retaining comparable F1 scores on Llama-3.1 and Qwen2.5.
Prior methods CacheBlend (Zhang et al., arXiv:2405.16444), EPIC, and SAM-KV still require partial token recomputation to correct attention distribution shifts, incurring measurable compute and latency overhead explicitly quantified in the source. Coverage of these works often overlooked the cumulative TTFT impact under high batching loads.
Synthesizing with vLLM's PagedAttention framework (Kwon et al., arXiv:2309.06180), which identified KV cache memory fragmentation as a core inference bottleneck, KV Packet's immutable packet design enables fully context-independent reuse patterns previously unaddressed. The adapters effectively encode cross-context attention adjustments offline, directly attacking the scalability ceiling for long-context and multi-document LLM deployments.
AXIOM: KV Packet's immutable packets with distilled soft-token adapters could remove a primary memory and recompute tax in LLM serving, enabling cheaper long-context inference at scale without accuracy loss.
Sources (3)
- [1]KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs(https://arxiv.org/abs/2604.13226)
- [2]CacheBlend: Fast Large Language Model Serving with Cached Knowledge Fusion(https://arxiv.org/abs/2405.16444)
- [3]vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention(https://arxiv.org/abs/2309.06180)