Snowflake Research Publications

Explore the latest academic insights from Snowflake to discover how our commitment to open research drives enterprise-scale innovation.

AI

Arctic Extract: Compact, Efficient, and State-of-the-Art Vision-Language Processing

Arctic-Extract is a state-of-the-art model designed for extracting structural data (question answering, entities and tables) from scanned or digital-born business documents.

AI

Cortex AISQL: A Production SQL Engine for Unstructured Data

Snowflake's Cortex AISQL is a production SQL engine that integrates native semantic operations directly into SQL. This integration allows users to write declarative queries that combine relational operations with semantic reasoning, enabling them to query both structured and unstructured data effortlessly.

AI

What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment

Allison Sihan Jia, Daniel Huang, Nikhil Vytla, Nirvika Choudhury, Shayak Sen, John C Mitchell, Anupam Datta

AI

SuperOffload: Unleashing the Power of Large-Scale LLM Training on Superchips

Xinyu Lian, Masahiro Tanaka, Olatunji Ruwase, Minjia Zhang

AI

SuffixDecoding: Extreme Speculative Decoding for Emerging AI Applications

ARC ‘Challenge’ Is Not That Challenging

AI

STUN (Structured-Then-Unstructured) to deploy large models efficiently

Samyam Rajbhandari, Mert Hidayetoglu, Aurick Qiao, Ye Wang, Juncheng Yang, Jeff Rasley, Michael Wyatt, Yuxiong He

AI

Arctic-TILT. Business Document Understanding at Sub-Billion Scale

Łukasz Borchmann, Michał Pietruszka, Wojciech Jaśkowski, Dawid Jurkiewicz, Piotr Halama, Paweł Józiak, Łukasz Garncarek, Paweł Liskowski, Karolina Szyndler, Andrzej Gretkowski, Julita Ołtusek, Gabriela Nowakowska, Artur Zawłocki, Łukasz Duhr, Paweł Dyda, Michał Turski

AI

Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI

Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI

AI

Universal Checkpointing: A Flexible and Efficient Distributed Checkpointing System for Large-Scale DNN Training with Reconfigurable Parallelis

Xinyu Lian, Sam Ade Jacobs, Lev Kurilenko, Masahiro Tanaka, Stas Bekman, Olatunji Ruwase, Minjia Zhang

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