- The paper introduces a joint cache-token framework that partitions the KV cache between prefill recomputation and direct transfer to minimize handover latency.
- It derives explicit closed-form expressions and a dynamic scheduling policy to balance backhaul and compute delays among multiple users.
- Simulations demonstrate up to 3.7× latency reduction compared to traditional methods, confirming its scalability for 5G/6G edge deployments.
Low-Latency Edge LLM Handover via Joint KV Cache Transfer and Token Prefill: An Expert Analysis
Introduction
The paper "Low-Latency Edge LLM Handover via Joint KV Cache Transfer and Token Prefill" (2603.28018) addresses a critical issue in realizing seamless, low-latency LLM inference at the network edge for mobile users: how to minimize service interruption during handover (HO) between base stations (BSs). Autoregressive LLM streaming demands continuity of the key-value (KV) cache, which encodes partial context for token generation. Ensuring this continuity during HO is nontrivial, especially considering the computational and networking constraints in multi-user, edge-assigned scenarios.
The authors model a system in which K UEs undergo handover from a source to a target BS during Edge LLM token streaming. At HO, the streaming context—represented by the KV cache corresponding to all previously generated tokens—must be reconstructed before inference resumes. Two baseline solutions exist:
- Token-based HO (tHO): Transfers past tokens and re-prefills the target LLM, but this approach can lead to substantial time-to-first-token (TTFT) due to expensive recomputation.
- Cache-based HO (cHO): Transfers the complete KV cache over the backhaul, saving computation. However, the KV cache size, possibly on the order of hundreds of MBs for large context windows, creates bandwidth bottlenecks, especially with concurrent HOs.
The paper proposes a joint cache-token (ctHO) framework, which dynamically partitions the required KV cache between prefill recomputation and direct cache transfer, optimizing both for worst-user delay under backhaul and compute constraints.
The Joint Optimization Approach
The framework introduces a global optimization objective: minimize the maximum HO delay across all UEs by jointly selecting:
- The length L of the prefix to be reconstructed via prefill (enabling batch computation efficiencies), and
- The backhaul rate allocation for the remaining KV cache transfer.
The problem decomposes into two subproblems thanks to the monotonic characteristics of prefill and cache transfer delays with respect to L. The analysis establishes that the optimal L is obtained when the respective delays are equilibrated, or, if no intersection exists, at the corresponding endpoints (all-prefill or all-backhaul).
A key technical contribution is the derivation of explicit, closed-form expressions for the minimum cache transfer delay as a function of backhaul normalization and sequential UE handover times, leveraging cumulative demand scheduling principles. The authors further supply a constructive scheduling policy ensuring that all deadlines can be met with a dynamic, prioritized, one-at-a-time allocation across active UEs.
The proposed ctHO method demonstrates substantial empirical improvement. Simulations are conducted on a vehicular-like line network with four UEs per batch, Qwen2.5-7B-Instruct as the LLM (28 layers, 4 KV heads), and realistic cache sizes (176MB for a context of $3072$ tokens).
Key findings include:
- Up to 3.1× reduction in worst-user HO delay vs. cHO and 3.7× over tHO in settings where backhaul and compute bottlenecks are balanced.
- ctHO consistently yields minimal delays under varying backhaul capacities, prefill computational speeds, maximum cache sizes, and user counts.
- The delay benefits of ctHO persist as the system scales, with performance gaps widening in larger multi-user settings.
- Delays with/without HO are compared as a function of BS separation distance; as user-BS distance increases and the channel degrades, the HO-enabled methods outperform baseline non-HO schemes, underscoring the necessity for seamless HO as edge deployment becomes denser.
Theoretical and Practical Implications
This work provides a rigorous foundation for minimizing inference disruption during mobile handover in edge LLM settings. The joint optimization and explicit scheduling schemes are notable for being both theoretically grounded and implementable, guiding system design for 5G/6G edge deployments.
The paper's claims are supported by strong numerical evidence and explicit feasibility conditions, offering actionable scheduling and context transfer policies for operators. The framework is orthogonal to recent advances in KV cache compression [liu2024], quantization [Kivi2024], and distributed scheduling [Yet2026, Li2025], and could be integrated to further reduce delays or support heterogeneous LLMs.
Future Directions
Extending the current "hard HO" design, where streaming is interrupted, to "soft HO" (enabling overlap of decoding and prefill/computation at source and target) is a natural path forward and may alleviate interruption further. Additional research could explore integration with on-the-fly cache compression, adaptive context truncation, hierarchical edge-cloud handover, and application-specific service-level agreements (SLAs).
Conclusion
The ctHO framework unifies token prefill and cache transfer to minimize HO latency for mobile Edge LLM deployment, with stepwise-optimal scheduling and dynamic resource allocation. The work sets a clear benchmark for practical and theoretical approaches to seamless LLM streaming in mobility scenarios, paving the way for future advances in low-latency, distributed AI service delivery on next-generation networks.
