The IBM hybrid cloud platform is confronted with a complex challenge: hosting new artificial intelligence (AI) applications alongside nearly 2,000 existing internal workloads that comprise business operations at IBM.1 When hosting AI applications on a hybrid cloud platform, these requirements need to be considered:
- AI applications must comply with the company's intellectual property (IP) requirements.
- Developer tools need to be simple and foster productivity when used to create applications that utilize generative AI content.
- Sensitive data must be securely managed throughout ingestion and usage.
- Hosting environments must be cost-effective in terms of hardware resources, development and operations.
- Some AI workloads need to be hosted on-premises and require support for the same AI environments on local hardware.
Initially, the IBM Chief Information Officer (CIO) organization hosted a vector database and generative AI models that use provisioned, high-cost graphics processing units (GPUs). Though this option was effective, it wasn't optimal. During peak hours, competition for resources between Development and Production teams resulted in the acquisition of additional GPUs, which remained idle during off-peak hours.
Also, this complex environment required the operations team to dedicate extra time to manage AI clusters. This involved procuring resources, patching, and managing resiliency.
1IBM Hybrid Cloud applications data was obtained through the Superset IBM Application Portfolio Management (APM) ServiceNow Dashboard on September 30, 2024.