Papers by Krishna Teja Chitty-Venkata
PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference (2026.findings-eacl)
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Krishna Teja Chitty-Venkata, Jie Ye, Siddhisanket Raskar, Anthony Kougkas, Xian Sun, Murali Emani, Venkatram Vishwanath, Bogdan Nicolae
| Challenge: | Large Language Models (LLMs) are exploding to large sizes, including GPT, LLaMA, and DeepSeek. |
| Approach: | They propose a fine-grained, structured KV cache pruning strategy that enhances the memory efficiency of vLLM’s PagedAttention. |
| Outcome: | The proposed method integrates seamlessly with PagedAttention without any modifications to its CUDA attention kernels. |