A Survey on Efficient Inference for Large Language Models (2024 · 113 citations)
Running large language models is expensive. They need a lot of computation, memory traffic and storage, which raises latency, lowers throughput and makes deployment difficult on edge devices and costly in the cloud. This survey, by authors from Tsinghua University, Shanghai Jiaotong University and Infinigence-AI…
LLM Inference Unveiled: Survey and Roofline Model Insights (2024 · 91 citations)
This survey targets a practical gap in research on efficient LLM inference. Many acceleration techniques exist, but there has been no unified way to reason about why they help on real hardware. The authors build their review around the Roofline model. It relates a workload's demands to a device's compute capacity and…
VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models (2026 · 1 citation)
Small language models built for edge hardware still spend much of their memory on vocabulary-related parts, namely the embedding matrix and the LM head.

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Updated 2 November 2026