--- title: "Daniel Fu — author profile and papers" description: "Daniel Fu, University of California San Diego: 2 papers on Elamur Research (2023–2024), h-index 7 — affiliations, topics, co-authors and who cites them…" canonical: "https://research.elamur.ai/free/en/authors/daniel-fu/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/daniel-fu/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Daniel Fu # Daniel Fu Daniel Fu works on large language models from the systems side and the data side. Their papers here address two practical problems: how to run very large models on modest hardware, and how to give open research access to pretraining corpora that can be inspected and reproduced. The work sits within [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) and [GPU & parallel computing](https://md.research.elamur.ai/free/en/topics/gpu-parallel-computing/). It combines engineering of memory and computation with the building and documentation of large public datasets. The most cited of these papers is [FlexGen](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/) (2023). It presents an inference engine aimed at batch workloads, where response time matters less than total throughput. FlexGen treats GPU memory, CPU memory and disk as a single storage hierarchy. It uses linear programming to decide where model weights, activations and the attention cache should sit and when they should move, and it compresses parts of the model to 4 bits. This lets a single commodity GPU serve models that would normally need several accelerators. The second paper, [RedPajama](https://md.research.elamur.ai/free/en/papers/weber-2024-redpajama-open-dataset-training-large/) (2024), answers the lack of transparency around how leading models are trained. Its first release reconstructs the LLaMA-1 training mixture and records each ambiguous choice made along the way. Its second release offers a very large, lightly filtered Common Crawl corpus that comes with per-document quality signals, so users can build their own filtering. The newer of the two works moves from efficient inference toward open pretraining data. Fu is currently affiliated with the [University of California San Diego](https://md.research.elamur.ai/free/en/universities/university-of-california-san-diego/) and with Together, and earlier papers carry the [Stanford University](https://md.research.elamur.ai/free/en/universities/stanford-university/) byline. Co-authors on these papers include [Ying Sheng](https://md.research.elamur.ai/free/en/authors/ying-sheng/), [Christopher Re](https://md.research.elamur.ai/free/en/authors/christopher-re/) and [Percy Liang](https://md.research.elamur.ai/free/en/authors/percy-liang/). Daniel Fu has 2 papers in our library, in 2023–2024, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [GPU & parallel computing](https://md.research.elamur.ai/free/en/topics/gpu-parallel-computing/); often with [Anton Alexandrov](https://md.research.elamur.ai/free/en/authors/anton-alexandrov/), [Beidi Chen](https://md.research.elamur.ai/free/en/authors/beidi-chen/), [Ben Athiwaratkun](https://md.research.elamur.ai/free/en/authors/ben-athiwaratkun/). 2 papers in our library · 2023–2024 [OpenAlex](https://openalex.org/A5032865467) · [ORCID](https://orcid.org/0000-0002-2500-2577) · [**h-index 7**](https://openalex.org/authors/A5032865467) · [35 works](https://openalex.org/works?filter=author.id:A5032865467) · [371 citations](https://openalex.org/works?filter=author.id:A5032865467&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/daniel-fu/#papers) · [🔗 568 citations in our library](https://md.research.elamur.ai/free/en/authors/daniel-fu/cited-by/) · [⭐ Paper Star rank #2600](https://md.research.elamur.ai/free/en/paper-stars/page/26/#star-2600) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Daniel+Fu) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Daniel+Fu) · [dblp](https://dblp.org/search?q=Daniel+Fu) · [ACL Anthology](https://aclanthology.org/search/?q=Daniel+Fu) ## Affiliations [🎓**University of California San Diego** San Diego · United States 2025–2026 · 4 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-california-san-diego/) · [🤝**Together** Lacey · United States 2026 · 1 paper*current*](https://ror.org/01azqfh67) · [🎓**Stanford University** Stanford · United States 2019–2024 · 14 papers](https://md.research.elamur.ai/free/en/universities/stanford-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed. ## Topics [Language models & pretraining (2)](https://md.research.elamur.ai/free/en/topics/language-models/) · [GPU & parallel computing (1)](https://md.research.elamur.ai/free/en/topics/gpu-parallel-computing/) ## Works by year ### 2020s [**2024** 1 paper](https://md.research.elamur.ai/free/en/papers/weber-2024-redpajama-open-dataset-training-large/) · [**2023** 1 paper](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/) ## Papers The most cited first (OpenAlex and our library). 1. **[FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/)** *(2023 · 399 citations)* Running very large language models usually requires several expensive accelerators. GPT-175B alone needs about 325GB just for its weights. Many workloads, however, are batch jobs where latency matters little, such as benchmarking, information extraction and data wrangling. The authors build FlexGen, an inference… 2. **[RedPajama: an Open Dataset for Training Large Language Models](https://md.research.elamur.ai/free/en/papers/weber-2024-redpajama-open-dataset-training-large/)** *(2024 · 247 citations)* Many leading LLMs are trained on data whose sources and cleaning steps are never disclosed. This makes open research on pretraining corpora slow and hard to replicate. The authors respond with two public releases. RedPajama-V1 rebuilds the LLaMA-1 training mixture from its brief published description and records… ## Co-authors here 12 of 15 co-authors with a page, the most papers together first. - [Anton Alexandrov](https://md.research.elamur.ai/free/en/authors/anton-alexandrov/) - [Beidi Chen](https://md.research.elamur.ai/free/en/authors/beidi-chen/) - [Ben Athiwaratkun](https://md.research.elamur.ai/free/en/authors/ben-athiwaratkun/) - [Christopher Re](https://md.research.elamur.ai/free/en/authors/christopher-re/) - [Clark Barrett](https://md.research.elamur.ai/free/en/authors/clark-barrett/) - [Huu Nguyen](https://md.research.elamur.ai/free/en/authors/huu-nguyen/) - [Joseph E. Gonzalez](https://md.research.elamur.ai/free/en/authors/joseph-e-gonzalez/) - [Lianmin Zheng](https://md.research.elamur.ai/free/en/authors/lianmin-zheng/) - [Percy Liang](https://md.research.elamur.ai/free/en/authors/percy-liang/) - [Quentin Anthony](https://md.research.elamur.ai/free/en/authors/quentin-anthony/) - [Rahul Chalamala](https://md.research.elamur.ai/free/en/authors/rahul-chalamala/) - [Ying Sheng](https://md.research.elamur.ai/free/en/authors/ying-sheng/) ## Cited by Papers of our library cite their works 568 times (of the 140,884 papers whose bibliographies we read); [21 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/daniel-fu/cited-by/).