When people talk about AI system performance, most focus on model size — how many parameters it has. In practice, several architectural factors affect the final result far more than parameter count alone. Understanding these factors is what separates a system that works "okay" from one that works great — sometimes without increasing the budget at all.
Mixture of Experts (MoE): specialization instead of brute force
One of the most important architectural innovations is Mixture of Experts (MoE). Instead of one giant model handling every request, the model is divided into small, specialized "experts," and each request is routed only to the experts relevant to it. The result: enormous capability at a much lower computational cost, because only a small part of the model is active at any moment.
An illustrative example: certain models hold hundreds of billions of parameters but activate only a small fraction of them — say tens of billions — per inference. This delivers the capability of a giant model at the running cost of a much smaller one. It’s worth knowing there’s also overhead: routing between experts adds complexity, and for very simple tasks a regular "dense" model is sometimes more efficient.
Combining retrieval and generation
A strong architecture doesn’t have to rely only on what the model "remembers." Combining retrieval with generation — the basis of RAG systems — lets the model access current, accurate information in real time, instead of relying only on what it was trained on. This improves accuracy, reduces hallucinations, and lets you update knowledge without retraining the model.
Agentic systems and orchestration
A major trend is the shift from a single model answering a question to "agentic" systems that break a complex task into a sequence of steps, execute them, and self-correct along the way. Here performance depends less on a single model and more on orchestration: which task goes to which component, when a large model steps in versus a small one, and how memory and context are managed. Smart orchestration is often the real difference between success and failure.
What really determines performance
To sum up: model size is just one factor among many. Memory and context management, search methods, data quality, and orchestration between components — all of these sometimes matter more than the model itself. Organizations that invest in understanding the architecture achieve better results, even without significantly increasing their budget.
| Factor | Impact on performance |
|---|---|
| Architecture (MoE etc.) | High capability at lower cost |
| Retrieval | Up-to-date accuracy and fewer hallucinations |
| Memory and context management | Consistency and quality across a conversation |
| Orchestration | Correct routing of tasks between components |
| Data quality | The foundation for everything else — "garbage in, garbage out" |
Frequently asked questions
Building an AI system and unsure which architecture is right? We help design and build AI systems with a focus on the factors that truly affect performance — not just model size. Talk to us for an introductory call.