Database Perspective on LLM Inference Systems
Summary: A database-centric tutorial on LLM inference, reframing request lifecycles, model execution, hardware specialization, distributed scaling, and memory management as query-processing and optimization problems. Unifies these techniques across inference architectures and application goals. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. James Pan (Tsinghua University)
- 2. Guoliang Li (Tsinghua University)
BibTeX Citation
@article{pan_vldb25,
title = {{Database Perspective on LLM Inference Systems}},
author = {Pan, James and Li, Guoliang},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5504--5507},
doi = {10.14778/3750601.3750703},
url = {https://doi.org/10.14778/3750601.3750703},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,304 | VecBench: A Controllable Benchmark for Filtered Vector Search: [Experiments & Analysis] | 2026 | SIGMOD | 5.093636e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,101 | LLM for Data Management | 2024 | VLDB | 5.9774273e-05 |
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