Solving the scaling challenge: 3 proven strategies for your AI infrastructure

Solving the scaling challenge: 3 proven strategies for your AI infrastructure The common challenges of scaling AI 1. Optimizing GPU resources with GPU-as-a-Service Case study: Turkish Airlines Demo: Implementing GPU-as-a-Service 2. Improving control with Models-as-a-Service From Infrastructure-as-a-Service to Models-as-a-Service API-driven model access 3. Scaling inference with vLLM and llm-d The case for vLLM Introducing llm-d for distributed inference Demo: Scaling with llm-d Bringing it all together Learn more The adaptable enterprise: Why AI readiness is disruption readiness About the authors James Harmison Philip Hayes Will McGrath More like this How Red Hat OpenShift AI simplifies trust and compliance A 5-step playbook for unified automation and AI Technically Speaking | Platform engineering for AI agents Technically Speaking | Driving healthcare discoveries with AI Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Every team that starts experimenting with generative AI (gen AI) eventually runs into the same wall: scaling it. Running 1 or 2 models is simple enough. Running dozens, supporting hundreds of users, and keeping GPU costs under control, is something else entirely. Teams often find themselves juggling hardware requests, managing multiple versions of the same model, and trying to deliver performance that actually holds up in production. These are the same kinds of infrastructure and operations challenges we have seen in other workloads, but now applied to AI systems that demand far more resources and coordination. In this post, we look at 3 practical strategies that help organizations solve these scaling problems: GPU-as-a-Service to make better use of expensive GPU hardware Models-as-a-Service to give teams controlled and reliable access to shared models Scalable inference with vLLM and llm-d to achieve production-grade, run-time performance efficiently When moving from proof of concept to production, cost quickly becomes the first barrier. Running large models in production is expensive, especially when training, tuning, and inference workloads all compete for GPU capacity. Control is another challenge. IT teams must balance freedom to experiment with guardrails that strengthen security and compliance.