Model Releases
Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each
Dense models activate all of their parameters for each token, whereas a Mixture‑of‑Experts (MoE) model loads all experts into memory but activates only a subset—e.g., Nemotron 3.5 Lightning has 30 B t
Dense models activate all of their parameters for each token, whereas a Mixture‑of‑Experts (MoE) model loads all experts into memory but activates only a subset—e.g., Nemotron 3.5 Lightning has 30 B total parameters yet uses about 3 B per token—thus decoupling memory cost from compute. With the same total parameter count, MoE models achieve higher token throughput than dense counterparts, though the latency advantage shrinks under high concurrency. Deployment decisions should therefore weigh memory budget, concurrency needs, fine‑tuning strategy and quantization behavior rather than raw parameter size alone.
Related
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- Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study
- Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource
Source: NVIDIA Developer | 2026-09-15