Accès ouvert déclaré
2024
preprint
The infrastructure powering IBM's Gen AI model development
Talia Gershon, Seetharami Seelam, Brian Belgodere, Milton Bonilla, Lan Hoàng, Danny Barnett, I‐Hsin Chung, Apoorve Mohan, Ming‐Hung Chen, Lixiang Luo, R. E. Walkup, Constantinos Evangelinos, Shweta Salaria, M.B. Dombrowa, Yoonho Park, Apo Kayi, Liran Schour, Alim Alim, Ali Sydney, Pavlos Maniotis, Laurent Schares, Bernard Metzler, Bengi Karacali-Akyamac, Sophia Wen, Tatsuhiro Chiba, Sunyanan Choochotkaew, Takeshi Yoshimura, Claudia Misale, Tonia Elengikal, K Connor, Zhuoran Liu, Richard Molina, Lars Schneidenbach, James Caden, Christopher Laibinis, Carlos Fonseca, Vasily Tarasov, Sundararaman Swaminathan, Frank Schmuck, Scott Guthridge, J. Cohn, Marc Eshel, Paul Muench, Runyu Liu, W. David Pointer, Drew Wyskida, Bob Krull, Ray Rose, Brent Wolfe, William Cornejo, John Walter, Colm Malone, Clifford Perucci, F. Lorenzo Franco, Nigel Hinds, Bob Calio, Pavel Druyan, Robert Kilduff, John Kienle, Connor McStay, Andrew Figueroa, Matthew Connolly, Edie Fost, Gina Roma, Jake Fonseca, Ido Levy, Michele Payne, Ryan Schenkel, Amir Malki, Lion Schneider, Aniruddha Narkhede, Shekeba Moshref, Alexandra Kisin, Olga Dodin, Bill Rippon, Henry Wrieth, John Ganci, Johnny Colino, Donna Habeger-Rose, Rakesh Pandey, Aditya Gidh, Dennis Patterson, Samsuddin Salmani, R. Varma, Rumana Rumana, Shubham Sharma, Mayank Mishra, Rameswar Panda, Aditya Prasad, Matt Stallone, Gaoyuan Zhang, Yikang Shen, David Cox, Ruchir Puri, Dakshi Agrawal, Drew Thorstensen, Joel Belog, Brent Tang, Saurabh Kumar Gupta, Anup Maheshwari, Eran Gampel, Jason Van Patten, Matthew Runion, Sai Kaki, Yigal Bogin, Brian Reitz, Steve Pritko, Shahan Najam, Surya Nambala, Radhika Chirra, Rick Welp, Frank DiMitri, Felipe Telles, Amilcar Arvelo, King Chu, Ed Seminaro, A. Schram, Felix Eickhoff, William A. Hanson, Eric Mckeever, Dinakaran Joseph, Piyush Chaudhary, Piyush Shivam, Puneet Chaudhary, Wesley Jones, Robert Guthrie, Chris Bostic, Rezaul Islam, Steve Duersch, Wayne Sawdon, John Lewars, Matthew Klos, Michael Spriggs, Bill McMillan, George F. Gao, Ashish Kamra, Gaurav Singh, Marc Curry, Tushar Katarki, Joe Talerico, Zenghui Shi, Sai Sindhur Malleni, Erwan Gallen
1Citations signalées, ce qui n’est pas une note de qualité
0Institutions déclarées
0Pays d’affiliation déclarés
Le résumé fourni par la source
AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
La source scientifique ouverte est momentanément indisponible.
Les sujets associés
Robotics and Automated SystemsEvolutionary Algorithms and ApplicationsDigital Transformation in Industry