The efficient enterprise: Scaling intelligence with Mixture of Experts

The efficient enterprise: Scaling intelligence with Mixture of Experts What is a Mixture of Experts? MoE is a distributed systems challenge KServe enables scalable serving for specialized experts Red Hat AI provides the enterprise control plane vLLM: High performance execution llm-d: Intelligent routing and observability From models to intelligence platforms The adaptable enterprise: Why AI readiness is disruption readiness About the authors Christopher Nuland Carlos Condado More like this Red Hat and NVIDIA collaborate for a more secure foundation for the agent-ready workforce Bringing Nemotron models to the Red Hat AI Factory with NVIDIA Technically Speaking | Build a production-ready AI toolbox Technically Speaking | Platform engineering for AI agents Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share As organizations scale generative AI (gen AI) across business units, a familiar tension appears—bigger models can often deliver better results, but they also require significantly more compute, cost, and operational complexity. This creates a production paradox— while enterprises want higher-quality reasoning, domain specialization, and agentic autonomy, they struggle to deploy monolithic trillion-parameter models that run continuously across clusters. As a result, the industry is shifting strategies, moving from single, massive models toward more efficient architectures. One of those technologies is Mixture of Experts (MoE). When MoE is combined with an enterprise AI platform like Red Hat AI, the result is not simply better model performance, it becomes a fundamentally different operating model for enterprise intelligence. Imagine a college campus. If you have a physics question, you would not walk into the history department, admissions office, or dining hall. You would go directly to the physics building where the right experts are located. MoE models follow the same principle. Instead of activating one massive neural network for every request, an MoE model introduces: Many specialized expert subnetworks trained for different reasoning patterns A routing mechanism that selects which experts should participate Sparse activation , so only a subset of parameters runs for each token This design enables the system to behave like a very large model while consuming resources like a much smaller one. The practical outcome is higher effective capacity, lower compute per inference, and improved scaling economics. MoE is not only a modeling technique, it is also a distributed systems challenge.