# Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each

DevFeed: [Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each](<https://devfeed.tech/articles/dense-vs-moe-models-active-parameters-throughput-and-when-to-choose-each-26912.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/dense-vs-moe-models-active-parameters-throughput-and-when-to-choose-each/>)

Author: Elizabeth Goodman

Published: 2026-09-15T17:00:11Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [cost](<https://devfeed.tech/tags/cost.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [llms](<https://devfeed.tech/tags/llms.md>), [memory](<https://devfeed.tech/tags/memory.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [models](<https://devfeed.tech/tags/models.md>), [moe](<https://devfeed.tech/tags/moe.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [performance](<https://devfeed.tech/tags/performance.md>), [router](<https://devfeed.tech/tags/router.md>), [routing](<https://devfeed.tech/tags/routing.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

## AI overview

This article explains how dense and Mixture-of-Experts models activate parameters, compares their effects on throughput, memory cost, and serving complexity, and discusses when each architecture fits different deployment constraints. It uses Nemotron 3.5 Lightning as an example of an MoE model.

## Source excerpt

How can a 30B-parameter model activate only 3B parameters per token, and still use the capacity of the larger model? Nemotron 3.5 Lightning illustrates the...