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NEWS · MODELS · #91

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

An NVIDIA Developer post compares dense and Mixture-of-Experts (MoE) architectures, showing how a 30B-parameter model can activate only about 3B parameters per token and discussing the resulting capacity and throughput trade-offs; Nemotron 3.5 Lightning is used as an illustrative example.

KEY POINTS

  1. An NVIDIA Developer post compares dense and Mixture-of-Experts (MoE) architectures, showing how a 30B-parameter model can activate only about 3B parameters per token and discussing the resulting capacity and throughput trade-offs; Nemotron 3.5 Lightning is used as an illustrative example.
  2. Architecture choices between dense and MoE directly affect model capacity, compute efficiency, and inference throughput, so practical explanations and examples help practitioners decide which to use.
  3. Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each

WHY IT MATTERS

Architecture choices between dense and MoE directly affect model capacity, compute efficiency, and inference throughput, so practical explanations and examples help practitioners decide which to use.

SOURCES & TIMELINE

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NVIDIA DEVELOPER FIRST-PARTY
Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each

Nemotron 3.5 Lightning uses a Mixture-of-Experts architecture that activates only 3B of its 30B total parameters per token. MoE models decouple memory cost from compute cost by loading all experts into VRAM while activating only a subset per token, unlike dense models where both scale together. At equal total parameter counts, MoE models such as Nemotron 3.5 Lightning achieve higher token throughput than dense mode…

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