Efficient Remote KV Cache Reuse with GPU-native Video Codec
KVCodec leverages GPU-native video codecs to compress remote KV caches, reducing TTFT by up to 3.51× while maintaining accuracy.
Key Findings
Methodology
This paper introduces KVCodec, a system integrating GPU-native video encoding hardware for KV cache compression. It designs a codec-friendly tensor layout that maps KV tensors into video frames, exploiting spatial and temporal redundancies for lossless compression. The system includes a fetch-aware scheduler that discriminates requests, an adaptive resolution mechanism for bandwidth tuning, and a fast GPU decoding pipeline. The architecture enables pipelined transmission, decoding, and restoration of KV caches, minimizing resource contention and masking network jitter. Extensive experiments across multiple GPUs and models (7B-70B) demonstrate TTFT reductions up to 3.51×, with no loss in inference accuracy.
Key Results
- In multi-GPU setups, KVCodec achieves up to 3.51× reduction in TTFT, with compression ratios exceeding 10× compared to traditional methods. Bandwidth savings surpass 60%, and decoding latency is reduced by over 40%. The system maintains model accuracy, demonstrating robustness across bandwidths from 1 to 40Gbps. It performs well on low-end GPUs, validating broad deployment potential.
- The novel tensor layout, which slices KV tensors along token dimension and arranges them across multiple frames, yields significant compression gains. The scheduling strategy isolates KV fetch requests, reducing blocking and resource contention. Multi-resolution adjustment dynamically adapts to network conditions, optimizing transfer efficiency. These innovations collectively enable high-speed, low-latency remote KV cache reuse.
- Experimental results confirm that KVCodec outperforms existing solutions like CacheGen and ShadowServe, which suffer from either low compression ratios or high decompression overhead. The system's design ensures interference-free inference for non-reuse requests, preserving accuracy while accelerating the first token generation. Its scalability and efficiency make it suitable for industrial-scale deployment.
Significance
This work pioneers the integration of GPU-native video codecs into remote KV cache management, addressing a critical bottleneck in large-scale LLM inference. By exploiting hardware designed for video processing, it achieves high compression efficiency without additional hardware costs. The approach significantly reduces latency, enabling faster response times in cloud services and edge deployments. It also offers a cost-effective solution for bandwidth-limited environments, facilitating broader adoption of large models in real-world applications. The methodology opens new avenues for hardware-software co-design, leveraging existing GPU resources for AI acceleration beyond traditional compute tasks.
Technical Contribution
The paper introduces a novel tensor layout tailored for video codecs, enabling lossless compression of KV caches by fully utilizing intra- and inter-frame redundancies. It designs a request-aware scheduler that prevents resource contention and network jitter effects. The multi-resolution adjustment mechanism dynamically tunes video resolution based on bandwidth, optimizing transfer speed. The system seamlessly integrates GPU hardware acceleration for encoding and decoding, achieving high throughput with minimal latency. These innovations collectively form a scalable, efficient framework for remote KV cache reuse, setting a new standard for hardware-aware AI system design.
Novelty
This research is the first to leverage GPU-native video codecs specifically for KV cache compression in large language model inference. Unlike prior work that treats KV tensors as generic byte streams, this approach designs a tensor layout aligned with video encoding principles, maximizing redundancy exploitation. The integration of adaptive resolution and request-aware scheduling further distinguishes it from existing systems, enabling real-time, high-efficiency remote KV cache management. This combination of hardware-aware compression and intelligent scheduling represents a significant leap forward in AI system engineering.
Limitations
- The system heavily depends on GPU hardware support for video encoding/decoding, which may limit portability across different vendors or future hardware generations. Compatibility issues could arise in heterogeneous environments.
- While lossless compression preserves accuracy, extreme compression ratios might still risk minor information loss, especially with aggressive layout adjustments. Fine-tuning parameters is necessary to balance compression and fidelity.
- In scenarios with extremely high request concurrency or very low bandwidth, the current adaptive resolution may not fully compensate, requiring further optimization of scheduling and resource management.
Future Work
Future directions include enhancing multi-GPU coordination for even larger-scale deployments, developing more adaptive and intelligent bandwidth-aware调度策略，以及结合模型剪枝和量化技术，进一步降低存储和传输成本。此外，将探索支持多种硬件平台的通用方案，提升系统的兼容性和鲁棒性。还计划在极端网络环境中优化调度算法，以确保系统在各种实际场景下都能实现低延迟和高效率。
AI Executive Summary
Large Language Models (LLMs) have revolutionized AI applications, but their inference speed remains a bottleneck, especially when handling extensive context windows. KV caches, which store intermediate attention states, are vital for accelerating autoregressive decoding. However, in distributed cloud environments, fetching these caches over limited-bandwidth networks introduces significant latency, hindering real-time performance.
Existing solutions like CacheGen and ShadowServe attempt to compress KV caches to reduce transmission costs. Yet, these methods face trade-offs: lossy compression degrades accuracy, while lossless approaches offer limited compression ratios. Moreover, integrating video codecs into KV compression has been explored but remains suboptimal due to inefficient layouts and resource contention, leading to minimal latency improvements.
This paper introduces KVCodec, a novel system that exploits GPU-native video codecs for high-efficiency KV cache compression and transmission. By designing a codec-friendly tensor layout that aligns with video encoding principles, KVCodec achieves lossless compression ratios over ten times higher than traditional methods. Its architecture incorporates a fetch-aware scheduler that intelligently discriminates requests, and a multi-resolution adjustment mechanism that dynamically tunes video resolution based on network conditions. These innovations enable pipelined transmission, decoding, and restoration, significantly reducing the model’s first token latency (TTFT) by up to 3.51×.
Extensive experiments across multiple GPU platforms and models (7B to 70B parameters) demonstrate that KVCodec maintains model accuracy while delivering substantial latency reductions. The system adapts seamlessly to various bandwidths (1-40Gbps), showing robustness in both high-end and resource-constrained environments. Its scalable design and hardware utilization efficiency suggest broad applicability in cloud and edge AI deployments.
Overall, KVCodec represents a significant step forward in hardware-aware AI system design, leveraging existing GPU video encoding hardware to address core bottlenecks in large-scale LLM inference. Future work will focus on multi-GPU coordination, adaptive scheduling, and integrating compression with model optimization techniques, aiming to further accelerate AI services and expand their practical reach.
Deep Analysis
Background
近年来，随着大规模语言模型（LLMs）不断扩展，推理速度成为制约其实际应用的关键因素。KV缓存技术通过存储Attention层中间状态，有效减少重复计算，显著提升推理效率。早期方法如DeepSpeed、Megatron-LM提出多级缓存优化，但在云端分布式环境中，远程存储和带宽限制成为新的瓶颈。传统压缩方案如算术编码虽节省带宽，却带来高解压延迟，影响整体响应时间。GPU中的视频编码硬件（如NVIDIA NVENC）具有高效利用空间和时间冗余的能力，为KV缓存压缩提供新思路。然而，如何将视频编码技术与KV缓存高效结合，仍待深入探索。
Core Problem
远程KV缓存复用面临两个核心挑战：一是如何在保证推理精度的前提下，实现高压缩比以降低传输延迟；二是在有限带宽条件下，快速、无干扰地传输和恢复KV缓存。现有方案在压缩比和解压速度之间难以兼顾，导致TTFT难以显著缩短。同时，不合理的调度策略会引发资源争用，网络抖动和硬件资源竞争严重影响系统性能，限制大规模部署的可行性。
Innovation
本研究的创新点包括：1）设计面向KV张量的GPU原生视频编码布局，充分利用空间和时间冗余，实现无损压缩比提升10倍；2）引入请求识别与调度隔离机制，有效避免资源争用和网络抖动影响；3）采用多分辨率动态调节策略，根据带宽变化调整视频分辨率，优化传输效率。这些设计结合硬件特性，突破传统压缩方案的局限，显著降低TTFT，提升系统鲁棒性。
Methodology
- �� 将KV张量重塑为视频格式，采用跳过DCT和量化的无损布局，最大化空间与时间冗余。
- �� 分析模型中Token维度的空间相似性，采用多帧连续布局，提升压缩比。
- �� 设计请求识别调度器，优先调度KV重用请求，减少非重用请求阻塞。
- �� 实现多分辨率调节，根据网络带宽动态调整视频分辨率，平衡传输速度与解码效率。
- �� 利用GPU原生视频解码硬件，快速还原KV张量，减少内存占用。
- �� 将压缩、调度、解码模块集成到推理框架中，支持多GPU环境，确保系统高效运行。
Experiments
在NVIDIA H20、A100等GPU平台上，测试7B、13B、70B模型，带宽范围1-40Gbps。比较全预填充、原始KV复用及现有压缩方案，指标包括TTFT、压缩比、模型精度。调节压缩参数和调度策略，验证在不同带宽和请求负载下的性能表现。还进行鲁棒性测试，确保网络抖动时系统依然稳定高效。
Results
KVCodec在多GPU平台上将TTFT降低最高3.51倍，压缩比提升10倍，模型精度无损。带宽限制条件下，传输与恢复时间显著缩短，系统表现出极强的鲁棒性。调度机制有效隔离非重用请求，减少资源争用。多分辨率调节实现动态适应网络变化，确保在不同环境中都能实现低延迟。系统在低端GPU上依然表现优异，验证了其广泛适用性。
Applications
该方案适用于云端大模型推理、边缘设备部署等场景，尤其在带宽有限或成本敏感环境中。通过降低传输延迟，改善用户体验，减少存储和带宽成本。未来可结合模型剪枝、量化技术，进一步优化存储和传输效率，推动大模型的普及。
Limitations & Outlook
系统依赖GPU硬件支持的视频编码能力，可能在不同硬件平台上存在兼容性问题。极端压缩比可能引入微小信息丢失风险，需调节参数平衡压缩与精度。调度策略在高并发或极低带宽环境下仍需优化，未来需增强自适应能力。系统在极端低端GPU上的性能表现尚未充分验证，未来将关注多平台兼容性和成本优化。
Plain Language Accessible to non-experts
想象你在一个大厨房里，厨师每天都要准备大量的食材（数据）。为了节省时间，他们会把一些重复的步骤提前做好（缓存），这样以后用的时候就不用重新做了。但是，这些食材很大，传输和存储都很麻烦。于是，厨师决定用一种特别的方法，把这些食材变成一种紧凑的“快递包裹”，用类似视频压缩的技术，把它们变得更小、更快传递。厨师还设计了一个聪明的调度系统，确保在传输和解包的过程中，不会影响厨房的正常工作。这样一来，厨房的效率大大提高，等待时间缩短了很多。这就像用视频编码技术，把复杂的食材压缩成小包裹，然后快速解压出来，帮厨师更快完成任务！
ELI14 Explained like you're 14
想象你在玩一个超级复杂的拼图游戏，你需要很多相似的拼图片段。每次拼完一部分，你都可以把它们存起来，以后再用。可是，这些拼图片段很大，传输到你的拼图桌上需要很长时间。于是，你的朋友告诉你一个秘密：你可以用一种特别的“压缩包”把这些拼图片段变得更小、更快传输，就像视频一样。你还设计了一个聪明的调度系统，确保在传输这些压缩包时，不会影响你拼图的速度。这样一来，你就能更快完成拼图，节省时间，而且拼图的质量也不会变差。这就像用视频编码技术，把大块的拼图片段压缩成小包裹，然后快速解压出来，帮你更快完成拼图！
Abstract
Remote KV cache reuse fetches KV cache for identical contexts from remote storage, avoiding recomputation, accelerating LLM inference. While it excels in high-speed networks, its performance degrades significantly in bandwidth-limited scenarios. Recent studies address this by transmitting KV caches in compressed form, but the associated heavyweight decompression counteracts the KV reuse benefits. In this paper, we propose an efficient and widely deployable remote KV cache reuse solution that leverages GPU-native video codecs. Our system, KVCodec, enables effective KV cache coding with two techniques. The codec-friendly tensor layout compresses the KV cache in a highly compact video format, enabling fast transmission. The efficient KV fetcher orchestrates the transmission, decoding, and restoration of compressed KV caches in an efficient pipelined manner, eliminating resource contention, masking network fluctuations, and achieving minimum time-to-first-token (TTFT). We prototype KVCodec on diverse GPUs from high- to low-end. Experiments reveal that it reduces TTFT by up to 3.51 times while maintaining lossless accuracy, compared to SOTA methods.