FastKV Accelerates Long Text Processing with Efficient KV Cache Compression

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Faster Processing of Long Texts through Efficient KV Cache Compression: FastKV
Large Language Models (LLMs) have revolutionized text processing, enabling impressive performance in areas such as text generation, translation, and question answering. A central aspect of these models is their ability to process long sequences while considering context across many tokens. This capability is enabled by the so-called Key-Value (KV) cache, which stores contextual information. However, the size of this KV cache presents a significant challenge, both in terms of memory requirements and computational power. Previous approaches to compressing the KV cache focused primarily on reducing memory footprint but neglected to improve latency.
A new approach called FastKV now promises a remedy. This method for KV cache compression was specifically designed to reduce latency in processing long sequences while preserving the accuracy of the LLMs. The core of FastKV lies in what is called Token-Selective Propagation (TSP). This innovative approach retains the complete contextual information in the initial layers of the LLM and selectively propagates only a portion of this information in the deeper layers, even in the prefill phase. This significantly reduces the computational load in the later layers without significantly impacting accuracy.
In addition to TSP, FastKV integrates grouped-query attention (GQA)-aware KV cache compression. GQA is a technique that improves the efficiency of attention mechanisms in LLMs. By considering GQA when compressing the KV cache, FastKV can optimally leverage the benefits of this technique in terms of both memory requirements and computational performance.
Initial experimental results show promising outcomes. Compared to HeadKV, an established method for KV cache compression, FastKV achieves a two-fold improvement in Time-to-First-Token (TTFT) and a 1.4-fold improvement in throughput. At the same time, accuracy on long-sequence benchmarks remains at a level comparable to the base models. These results underscore the potential of FastKV to significantly increase the efficiency of LLMs in processing long texts without compromising accuracy.
The combination of TSP and GQA integration allows FastKV to address the challenges of KV cache compression in an innovative way. By selectively propagating contextual information and leveraging the advantages of GQA, FastKV offers a promising approach to optimizing the latency and throughput of LLMs. This opens up new possibilities for the use of LLMs in applications that require the processing of long texts, such as document analysis, information summarization, and the generation of longer texts.
The development of FastKV highlights the ongoing efforts of the research community to improve the efficiency and scalability of LLMs. With the increasing demand for more powerful language models, innovative approaches like FastKV are essential to push the boundaries of what is possible and unlock new application scenarios. The availability of the code on GitHub allows researchers and developers to test FastKV and integrate it into their own projects.
Bibliography: Jo, D., Song, J., Kim, Y., & Kim, J.-J. (2025). FastKV: KV Cache Compression for Fast Long-Context Processing with Token-Selective Propagation. Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025). Stanford, K. (2024). Title of Stanford Paper. *Proceedings of SIGCOMM 2024*. Further Source 1. Further Source 2. Further Source 3. Further Source 4. Further Source 5. Further Source 6.