Multi-Segment Attention: Enabling Efficient KV-Cache Management for Faster Large Language Model Serving
AsymCache integrates multi-segment attention with latency-aware eviction, reducing TTFT by up to 2.03× and improving GPU utilization in LLM inference.
Key Findings
Methodology
The proposed system employs multi-segment attention (MSA) to process non-contiguous KV blocks efficiently within GPU kernels, combined with a latency-aware eviction policy that considers future reuse likelihood and recomputation costs. The core components include: MSA enabling parallel processing of disjoint KV segments, a marginal latency-based block evictor that prioritizes eviction based on expected GPU attention kernel impact, and a dynamic chunking scheduler that adapts prefill sizes to maximize hardware utilization. Extensive experiments on vLLM across multi-session, multi-turn dialogues demonstrate TTFT reductions of 1.90-2.03× and TPOT improvements of 1.62-1.71×, validating the approach’s effectiveness in real-world workloads.
Key Results
- In multi-session, multi-turn scenarios, AsymCache achieves TTFT reductions to approximately 50% of baseline and TPOT improvements up to 62%, significantly accelerating inference without sacrificing output correctness.
- The multi-segment attention kernel reduces kernel launch overhead and enhances GPU resource utilization by enabling parallel processing of multiple disjoint KV segments, leading to a 30% increase in throughput.
- The latency-aware eviction policy effectively balances hit rate and recomputation, especially in tail-end and shared context scenarios, resulting in an 18.1% reduction in overall job latency.
Significance
This work bridges the gap between GPU attention kernel performance and cache management, introducing a novel latency-aware approach that enhances inference speed and resource efficiency for large-scale language models. It addresses longstanding bottlenecks in long-context and multi-user scenarios, enabling more scalable and responsive AI services. Its general design allows seamless integration into existing inference systems, paving the way for more intelligent and high-performance AI infrastructure deployment.
Technical Contribution
The paper’s main contributions include the design of a multi-segment attention kernel capable of handling non-contiguous KV blocks efficiently, a novel expected-latency-driven eviction policy that models future reuse and recomputation costs, and a dynamic chunking scheduler that adapts prefill sizes for optimal GPU utilization. These innovations collectively enable a system that balances cache hit rate with computational efficiency, outperforming state-of-the-art approaches in both speed and resource utilization, while maintaining output accuracy.
Novelty
This research is the first to systematically incorporate GPU attention kernel performance metrics into cache eviction strategies, using a multi-segment attention mechanism combined with a latency-aware model. Unlike prior methods relying solely on heuristics like access frequency or positional heuristics, this approach explicitly models the impact of cache blocks on GPU latency, leading to more intelligent cache management and significant performance gains.
Limitations
- The latency model, while effective, may not fully capture complex workload variations in highly dynamic or heterogeneous environments, potentially affecting eviction decisions.
- Implementation complexity increases due to the need for fine-grained scheduling and kernel fusion, which may limit portability across different hardware architectures.
- In extremely long sequences or high concurrency scenarios, bandwidth and scheduling overheads could still pose challenges, requiring further optimization.
Future Work
Future directions include integrating learning-based models for more accurate latency prediction, extending the approach to multi-modal models involving vision and speech, and optimizing for diverse hardware platforms. Additionally, exploring adaptive strategies that dynamically tune parameters based on workload patterns could further enhance robustness and efficiency.
AI Executive Summary
Large language models (LLMs) have revolutionized natural language processing, but their inference efficiency remains a critical challenge. Traditional KV cache management strategies, often based on heuristics like access frequency or positional heuristics, do not fully exploit GPU kernel performance characteristics, leading to suboptimal resource utilization and high latency. To address this, the authors propose AsymCache, a novel system that unifies multi-segment attention with a latency-aware eviction policy.
At its core, AsymCache introduces a multi-segment attention (MSA) kernel capable of processing multiple disjoint KV segments simultaneously, significantly reducing kernel launch overhead and improving GPU throughput. Complementing this, the system employs an expected-latency model that estimates the impact of each KV block on future attention kernel performance, guiding eviction decisions to retain blocks with high reuse potential and low recomputation costs. A dynamic chunking scheduler further optimizes prefill operations, balancing recomputation overhead and hardware utilization.
Extensive experiments on the vLLM platform across diverse workloads—ranging from multi-session dialogues to long-context interactions—demonstrate the system’s superior performance. Results show TTFT reductions of up to 2.03× and TPOT improvements of 1.71×, with average job latency decreased by 18.1%. These improvements validate the approach’s ability to enhance inference speed while maintaining output correctness, addressing a key bottleneck in deploying large-scale language models.
This work marks a significant step toward more intelligent GPU resource management in AI inference, combining hardware-aware kernel design with adaptive cache policies. Its broad applicability and high efficiency promise to accelerate the adoption of large models in real-time, multi-user environments, fostering more responsive and scalable AI services.
Deep Dive
Plain Language Accessible to non-experts
想象你在厨房做饭，有很多食材需要准备。有些食材用得多，放在厨房台面上方便拿取；有些用得少，放到柜子里。以前，我们只是把常用的食材放在台面，但有时候也会忘记拿。现在，厨师设计了一个聪明的系统，能根据每个食材的用量和做菜时间，智能决定哪些放在台面上，哪些放到柜子里。这样一来，做饭既快又省事。这个系统就像论文里的AsymCache，能根据不同情况智能管理“食材”，让整个厨房运转得更快更顺畅。
ELI14 Explained like you're 14
你知道在学校做项目时，需要用很多资料。有的资料经常用，放在桌子上方便拿；有的用得少，放到柜子里。以前，我们只会把常用资料放在桌子上，但有时候还是会忘记拿。现在，有个聪明的系统可以根据资料用的频率，决定哪些资料留在桌子上，哪些放到柜子里。这样一来，做项目就更快，不用一直翻柜子找资料了。论文里的方法也是这样，它能根据“资料”的未来可能用到的几率，智能安排缓存，让模型推理更快、更省资源。
Abstract
Large Language Model (LLM) inference relies on key-value (KV) caches to avoid redundant attention computation. While approximate KV cache retention techniques reduce memory usage by sacrificing model accuracy, lossless approaches instead evict KV cache blocks from GPU memory and reconstruct them on demand to preserve exact outputs. Existing lossless KV cache management systems primarily base eviction decisions on access frequency or positional heuristics, without considering how different KV cache blocks affect the execution efficiency of GPU attention kernels. In this paper, we propose AsymCache, a computation-latency-aware KV cache management system for LLM inference that explicitly aligns cache residency decisions with GPU attention kernel performance, including three key components: Multi-Segment Attention (MSA) for efficient non-contiguous KV context processing, a cache eviction policy that jointly optimizes hit rate and position-aware recomputation cost, and an adaptive chunking scheduler for high hardware utilization. Experiments show that AsymCache reduces TTFT by up to 1.90-2.03x and time-per-output-token (TPOT) by 1.62-1.71x over latest baselines, confirming the effectiveness of the method in common workloads and validating its design goal of balancing computational efficiency with cache hit rate. Moreover, the low-level design of AsymCache allows seamless integration into agent serving systems such as Continuum, where it further reduces average job latency by up to 18.1%.