Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each
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Elizabeth Goodman
Author
Elizabeth Goodman
Date
Terms in this piece · Glossary
mixture-of-experts — A model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.
quantization — Shrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.
fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
Why it matters
Explains the concrete tradeoffs (memory vs compute cost, fine-tuningTaking a trained model and training it a bit more on your own examples so it gets better at one specific job.Full definition → router-imbalance risk, quantizationShrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.Full definition → sensitivity) that should drive whether an agentic engineer deploys a dense or mixture-of-expertsA model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.Full definition → model for a given workload.
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