Tap the Power of Turing AI Computing Cloud

Scalable AI infrastructure designed for evolving machine learning researches

Turing AI Computing Cloud (TACC) is a cloud platform for research and education in machine learning with open access to the research community.

Developed by HKUST and initially deployed in 2020, TACC supports research work on a wide range of machine learning applications with high-performance and scalable infrastructure on both software- and hardware-level.

TACC differs significantly from typical cloud computing platforms such as Amazon AWS and Microsoft Azure.

TACC is tailored to the workflow of machine learning application, providing you with a more efficient process of managing, deploying and scaling compute-intensive machine learning jobs in a computing cluster.

Docs and Demos

Comprehensive docs and examples available for you to get started

Check on GitHub

Call for Pioneers

Early-adopter Application is now open! Join now and boost your AI research

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Whitepaper

Learn about our IaaS architecture tailored for machine learning tasks

Read the Whitepaper
Highlights on Turing AI Computing Cloud
76 users

joined the TACC beta-testing since 2021/05/24

1,870 tasks

submitted to TACC for processing

19,224 GPU hours

used for machine learning tasks

From Our Beta-testing Users
K. Xu
PhD Student, HKUST
We are building a distributed system that can efficiently train Graph Nueral Networks with near-linear scalability over billion-edge graphs. TACC provides us with a scalable and flexible setting where we can run small and large-scale experiments based on our demand.
Project: Scalable and Efficient GNN Training for Large Graphs
TACC Term of Service