Papers by Bogdan Nicolae

2 papers
PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference (2026.findings-eacl)

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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.
CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation (2025.emnlp-main)

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Challenge: Large foundation models have become huge, but they consume computational resources in pretraining.
Approach: They propose to replace full-size layers with compute-efficient auto-encoders that enforce low-rank activations throughout training.
Outcome: The proposed method reduces the computing cost by 2pmbtimes and improves training throughput by 1.86pmtime.

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