When ChatGPT put Generative AI (GenAI) center stage in November 2022, it propelled GPUs and Large Language Models (LLMs) into the spotlight, too. Everyone from board members to people in the street quickly grasped the potential of the shiny new tech. Many assume GPUs and LLMs create AI applications, although they don’t, and the confusion doesn’t end there.
GPUs are only half of the double act at the heart of GenAI and agentic AI. The other half, CPUs, are less glamourous because they have been around since the 1970s, yet they run compute platforms from smartphones to data centers and cloud.
This lack of appreciation of the chips’ different contributions and their interdependence is responsible for much of the confusion about the difference between AI-enabled and AI-native applications. The situation is not helped by both sometimes being described as “AI apps” as though the terms are interchangeable. Also, some parties willfully describe apps enabled by AI as “AI-native” to refresh older products and solutions.
What does AI-native really mean?
AI-native apps are designed from the ground up with AI as their core engine. An AI-enabled app is a traditional application with AI features “bolted on” to extend their functionality. Not surprisingly, native-AI apps require a shift in software architecture, and AI-native systems redefine workflows and offer capabilities that would not exist without AI.
This is where LLMs and GPUs come in, as elements of the much larger software system needed to create an AI-native application. The model’s end point is the start of a potentially valuable operational or business outcome. Its value depends on the coordination of all the elements in the system for successful execution.
AI-native systems are composed systems with a frontend, APIs, orchestration layers, databases memory, and tools, as well as the model end points. In fact, AI-native apps resemble a distributed microservices architecture in which the model is a single service, albeit an essential one, not the application itself.
CPUs drive AI outcomes
The CPU identifies relevant data and feeds it to the AI model which runs on GPUs. The GPUs are specialist accelerators designed to handle huge numbers of calculations in parallel, very fast, to train models and produce outputs known as inferences. The CPU also acts as system coordinator to make those inferences usable.
The CPU sequences tasks, handling data ingestion and preprocessing, queries to the vector database and assembly of context, construction of prompts and post-processing. It orchestrates the agents, calls tools, and manages API logic, security, and user sessions.
As organizations move towards agentic AI systems, the CPU’s role becomes greater because agents’ reasoning loops, decision trees, memory management, and workflow execution are all CPU-intensive operations.
Economic viability is crucial
Although models are the most the visible innovation in GenAI, the scalability and cost of the model’s output – the model’s usefulness and affordability – hinge on how efficiently the CPU runs the app in the AI-native system. As operations become more CPU-intensive, any bottleneck in the underpinning compute infrastructure equates to a restricted business outcome at the juncture where cost is becoming a critical factor.
This is because in 2025-26, the focus of GenAI is shifting from training AI models to how we leverage inferences. This has been likened to moving from the R&D phase to putting models’ output into production and reaping real-world business benefits, including return on investment. Enterprises need to innovate and fund their AI transformation just as the cost of core cloud compute is becoming harder to control due to factors including opaque pricing and obligatory service bundling from cloud companies, and “hyperscaler-first” roadmaps.
At this pivotal point, cost control is not a defensive move so much as the mechanism that makes investment in AI and subsequent business benefits possible. Enterprises must look beyond traditional cloud companies to platforms that are optimized to reduce costs, such as Vultr VX1 Cloud Compute. It claims up to 82% better performance per dollar than hyperscalers with a lower cost per virtual CPU (vCPU) and pricing tailored to applications’ workloads where predictable economics, operational consistency, and architectural flexibility are foundational requirements.
It’s essential that the interdependent roles of GPUs, LLMs, and CPUs are well understood as AI promises the biggest leap yet in human productivity, for organizations and individuals.