Transformers vs. Mixture of Experts: When to Use Each Architecture

Dense transformers route every token through every layer. The design is simple, stable, and easy to optimize at scale.

They are reliable in production, with predictable latency and straightforward batching. For most product teams, dense models are still the safest choice.

Mixture of Experts activates only a subset of parameters per token. That means you can scale model capacity without scaling inference cost linearly.

MoE shines in large-scale deployments, but it requires careful routing, load balancing, and monitoring to avoid expert collapse.

MoE introduces routing overhead and can create tail latency spikes if certain experts are overloaded.

Dense models are slower per token at the same parameter count, but they are often more stable under unpredictable traffic.

MoE training is more complex. You need strategies to ensure experts specialize and remain balanced over time.

If you do not have the infrastructure to train and evaluate MoE properly, a well-tuned dense model will outperform it in practice.

Choose dense transformers if you need predictability, simpler ops, and fast iteration.

Choose MoE if you operate at very large scale, can invest in training infrastructure, and need lower per-token cost.

If you are considering MoE, start with a narrow workload and a clear success metric. Run both architectures in parallel for a few weeks, compare cost and latency, and evaluate tail performance under load. MoE can look great on averages but still struggle with noisy traffic or skewed routing.

For dense models, the safest path is steady optimization: quantization, batching, and caching. Most teams can reach their cost goals without a full architecture shift. Treat MoE as a strategic move when scale or multilingual coverage demands it.

Is MoE always cheaper? Not always. Training cost and operational overhead can erase inference savings.

Can MoE improve quality? It can, especially in multilingual or diverse tasks, but only with strong routing and data balance.

Which is easier to serve? Dense transformers are simpler to serve and debug.