The infrastructure powering IBM's Gen AI model development
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 7 Jul 2024.
Builds on
7 of 23 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,829
- 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 621
- 2018Scaling Neural Machine Translationcited by 204
- 2024Granite Code Models: A Family of Open Foundation Models for Code Intelligencecited by 116
- 2024MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUscited by 111
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.