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Íñigo Goiri

Íñigo Goiri has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Chaojie Zhang, Esha Choukse.

2 papers in our library · 2023–2024

OpenAlex · ORCID · Wikidata · h-index 33 · 115 works · 4,424 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 483 times in our library · ⭐ Paper Stars ranking: #3847

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

🏢MicrosoftRedmond · United States2015–2026 · 47 paperscurrent🎓The University of Texas at AustinAustin · United States2026 · 1 papercurrent🎓Rutgers, The State University of New JerseyNew Brunswick · United States2011–2015 · 17 papers🔬Barcelona Supercomputing CenterBarcelona · Spain2008–2012 · 17 papers🎓Universitat Politècnica de CatalunyaBarcelona · Spain2008–2012 · 15 papers🎓University of Illinois Urbana-ChampaignUrbana · United States2024–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 3The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksHardware and Architecture · 2 worksInformation Systems · 2 works

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

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Splitwise: Efficient generative LLM inference using phase splitting (2023 · 379 citations)
    Serving generative LLMs at scale is costly and power-hungry, and GPUs are often over-provisioned to meet latency SLOs.
  2. DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency (2024 · 218 citations)
    Serving LLMs on GPUs under tight latency targets uses a lot of energy. Existing power-management schemes were built for conventional datacenter software and miss the specific traits of LLM inference. The authors first profile several open models on an NVIDIA DGX H100 server running vLLM, varying prompt and output…

 Co-authors here

2 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 483 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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