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The efficient long context inference with LLM requires the management of the substantial GPU memory due to the high demands for storage storage in cache of the key value (KV). Traditional KV cache compression techniques reduce the use of memory by selectively significant tokens pruning, often depending on attention scores. However, existing methods evaluate the importance of Token independently, overlooking crucial dependencies between tokens to preserve semantic coherence. For example, a model can retain keywords related to the subject while discarding contextually significant terms, which leads to the loss of information. This limitation highlights the need for a more structured approach for KV cache compression that considers Token and semantic integrity relationships.
Recent research has explored dynamic KV cache compression strategies to optimize memory use without compromising performance. Methods such as H2O and Snapkv use an evaluation based on attention to selectively retain critical tokens, while fragmentation approaches organize the text in semantically significant segments. Fragmentation has been widely used in NLP for tasks based on training and recovery, which guarantees contextual consistency. In addition, layer techniques such as Lisa and Dola improve the efficiency of the model by taking advantage of the structural ideas of different layers of transformers. While these advances improve memory efficiency, the incorporation of the Token dependence awareness in KV cache compression can further improve context retention and inference quality in LLM.
Researchers at Hong Kong University introduced Chunkkv, a KV cache compression method that brings together significant fragments instead of individually evaluating them. This approach retains essential semantic information while reducing memory overload. In addition, the reuse of the index in the form of a layer further optimizes computational efficiency. Evaluated in reference points such as Longbench, Needle-in-A-Haystack, GSM8K and Jailbreakv, Chunkkv demonstrated higher performance, improving accuracy in up to 10% under aggressive compression. Compared to existing methods, Chunkkv effectively retains the contextual meaning and improves efficiency, establishing it as a robust solution for long context inference in large language models.
With the growing context length of the LLM, KV cache compression is crucial for efficient inference, since it consumes a substantial GPU memory. Chunkkv is an approach that retains semantically rich Token fragments, reducing the use of memory while preserving critical information. Segments Tokens in significant groups and select the most informative parts using care scores. An index reuse method in the form of a layer optimizes efficiency by sharing compressed indices between layers. Experimental results show that Chunkkv significantly improves the similarity of the index between the layers compared to the previous methods such as Snapkv. This structured KV retention is aligned with the principles of learning in context, maintaining semantic coherence while optimizing memory use.
The study evaluates the effectiveness of Chunkkv in KV cache compression in two reference points: context learning (ICL) and long context tasks. For ICL, the GSM8K, GSM8K Test Study of many shots and Jailbreakv using models such as Llama-3.1-8b-Instruct and Deepseek-R1-Distill-Llama-8b. Chunkkv constantly exceeds other methods to maintain precision in several compression relationships. For long context, the study evaluates Longbench and Needle-in A-Haystack (NIAH), which shows the superior Chunkkv performance that preserves crucial information. In addition, index reuse experiments demonstrate improved efficiency, reducing latency and increased performance in an A40 GPU. In general, the results confirm Chunkkv's ability to optimize KV cache compression while maintaining the effectiveness of the model in different contexts and architectures.
In conclusion, the study examines the impact of fragmentation size of Chunkkv performance, maintaining the same experimental environments as Longbench. The results indicate a minimal performance variation in fragments sizes, with 10-20 that produce the best results. Extensive evaluations in Longbench and Niah confirm that a fragment size of 10 optimally balances semantic preservation and compression efficiency. Chunkkv effectively reduces the use of KV cache memory while retaining crucial information. In addition, the technique of reuse of the index in the form of layer improves computational efficiency, reduces latency by 20.7% and improves yield by 26.5%. These findings establish Chunkkv as an efficient kv cache compression method to implement LLM.
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Sana Hassan, a consulting intern in Marktechpost and double grade student in Iit Madras, passionate to apply technology and ai to address real world challenges. With great interest in solving practical problems, it provides a new perspective to the intersection of ai and real -life solutions.
