24 citations · 43 across the 13 of their papers we have counts for
5 papers · 1 filter
Efficiently Scaling LLM Reasoning with Certaindex
Yichao Fu, Junda Chen, Siqi Zhu +8
Test-time reasoning algorithms such as chain-of-thought, self-consistency, and MCTS enhance LLM problem-solving but can wastefully generate many tokens without improving accuracy.…
SuffixDecoding: Extreme Speculative Decoding for Emerging AI Applications
Gabriele Oliaro, Zhihao Jia, Daniel Campos +1
Speculative decoding is widely adopted to reduce latency in large language model (LLM) inference by leveraging smaller draft models capable of handling diverse user tasks. However,…
SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation
Aurick Qiao, Zhewei Yao, Samyam Rajbhandari +1
LLM inference for enterprise applications, such as summarization, RAG, and code-generation, typically observe much longer prompt than generations, leading to high prefill cost and…
STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning
Jaeseong Lee, seung-won hwang, Aurick Qiao +3
Mixture-of-experts (MoEs) have been adopted for reducing inference costs by sparsely activating experts in Large language models (LLMs). Despite this reduction, the massive number…
Efficient LLM Scheduling by Learning to Rank
Yichao Fu, Siqi Zhu, Runlong Su +3
In Large Language Model (LLM) inference, the output length of an LLM request is typically regarded as not known a priori. Consequently, most LLM serving systems employ a simple Fir…