Abstract
The demand for efficient Large Language Model (LLM) inference has surged with the rising adoption of Generative AI (GenAI) applications, particularly in areas such as agents and retrieval-augmented generation. Efficient inference serves two crucial purposes: it enables the deployment of LLM-centered applications that address critical business needs, while also facilitating rapid experimentation for researchers to extract valuable insights and new understandings. However, despite the field's rapid advancement and interdisciplinary nature, there remains a limited exchange of ideas and methodologies between production-facing practitioners and researchers seeking to experiment with new GenAI concepts quickly. To bridge this gap, we are introducing the first KDD workshop on Inference Optimization for Generative AI. Our goal is to create a collaborative platform where researchers and practitioners working across various use cases and stacks of efficient inference can come together to exchange research ideas, establish connections between different disciplines, and identify challenges and research questions that will shape future work.
| Original language | English (US) |
|---|---|
| Title of host publication | KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
| Publisher | Association for Computing Machinery |
| Pages | 6302-6303 |
| Number of pages | 2 |
| ISBN (Electronic) | 9798400714542 |
| DOIs | |
| State | Published - Aug 3 2025 |
| Externally published | Yes |
| Event | 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025 - Toronto, Canada Duration: Aug 3 2025 → Aug 7 2025 |
Publication series
| Name | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
|---|---|
| Volume | 2 |
| ISSN (Print) | 2154-817X |
Conference
| Conference | 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025 |
|---|---|
| Country/Territory | Canada |
| City | Toronto |
| Period | 8/3/25 → 8/7/25 |
Bibliographical note
Publisher Copyright:© 2025 Owner/Author.
Keywords
- generative ai
- inference optimization
- large language models
- workshop
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