Elamur Research⌕
☰
home / authors / Kan Zhu

Kan Zhu

Kan Zhu has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining, Efficient models & compression; often with Yilong Zhao, Baris Kasikci, Zihao Ye.

3 papers in our library · 2023–2024

OpenAlex · h-index 12 · 26 works · 617 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 433 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #4586

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of MichiganAnn Arbor · United States2002–2006 · 8 papers🏢BiogenCambridge · United States2020–2024 · 3 papers🎓University of WashingtonSeattle · United States2023–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArts and Humanities · 2 worksComputer Vision and Pattern Recognition · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksEngineering · 1 workComputational Theory and Mathematics · 1 workElectrical and Electronic Engineering · 1 workHardware and Architecture · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3 worksEfficient models & compression · 1 work

 Works by year

2020s

20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference (2024 · 203 citations)
    Generating text with very long contexts is slow because every new token requires reading the full KV cache from memory.
  2. Atom: Low-bit Quantization for Efficient and Accurate LLM Serving (2023 · 156 citations)
    LLM serving relies on batching to keep GPUs busy, and quantization can further reduce memory use and compute cost.
  3. NanoFlow: Towards Optimal Large Language Model Serving Throughput (2024 · 80 citations)
    LLM serving is usually assumed to be limited by memory bandwidth, because every generated token requires reading the model weights and the KV-cache.

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Quest: Query-Aware Sparsity for Efficient Long-Context LLM InferencearXiv:2406.10774Jiaming Tang, Yilong Zhao, Kan Zhu, et al.2024 · 203 citations
  2. Atom: Low-bit Quantization for Efficient and Accurate LLM ServingMLSys 2024Yilong Zhao, Chien-Yu Lin, Kan Zhu, et al.2023 · 156 citations
  3. NanoFlow: Towards Optimal Large Language Model Serving ThroughputOSDI 2025Kan Zhu, Yufei Gao, Yilong Zhao, et al.2024 · 80 citations
  4. BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching ↗arXiv (Cornell University) · no summary yetYilong Zhao et al.2024 · 16 citations
  5. BlendServe: Optimizing Offline Inference with Resource-Aware Batching ↗ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · no summary yetYilong Zhao et al.2026 · 2 citations
  6. Accelerating Large-Scale Reasoning Model Inference with Sparse Self-Speculative Decoding ↗arXiv:2512.01278 · no summary yetYilong Zhao et al.2025 · 2 citations

 Co-authors here

10 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 433 times (of the 149,439 papers whose bibliographies we read); 43 citing papers with a page here →.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/kan-zhu/