TranSQL+: Serving Large Language Models with SQL on Low-Resource Hardware
Summary: TranSQL+ compiles LLM computation graphs into pure SQL to run inference inside relational DBs, leveraging vectorized execution and out‑of‑core processing to avoid GPUs or external runtimes. Introduces ROW2COL for matrix joins and gets up to 20× lower prefill latency and 4× faster decoding on low‑memory, CPU‑only hardware. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Wenbo Sun
- 2. Qiming Guo
- 3. Wenlu Wang
- 4. Rihan Hai
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 185 | DuckDB: an Embeddable Analytical Database | 2019 | SIGMOD | 0.00036538405 |
| 1,279 | Towards Linear Algebra over Normalized Data | 2017 | VLDB | 0.00012868394 |
| 2,194 | Enabling and Optimizing Non-linear Feature Interactions in Factorized Linear Algebra | 2019 | SIGMOD | 9.3138337e-05 |
| 2,642 | Vertica-ML: Distributed Machine Learning in Vertica Database | 2020 | SIGMOD | 8.3851878e-05 |
| 4,409 | Declarative Recursive Computation on an RDBMS | 2019 | VLDB | 6.2104034e-05 |
| 4,495 | ClickHouse - Lightning Fast Analytics for Everyone | 2024 | VLDB | 6.1410277e-05 |
| 6,191 | Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra | 2021 | SIGMOD | 5.1642282e-05 |
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