Nemotron 3 Super uses a Mixture-of-Experts (MoE) architecture where the model has 120 billion total parameters but only 12 billion activate for each token, making it more efficient than dense models while maintaining quality.
In this architecture, different ‘experts’ (specialized parameter sets) handle different types of inputs. A gating mechanism routes each token to the most relevant experts, so the model never needs to process all 120 billion parameters at once. This approach reduces memory requirements and computational overhead compared to running a dense 120-billion-parameter model, while preserving the knowledge capacity of the larger parameter count.
When deploying Nemotron 3 Super embeddings with Milvus, the efficient token processing means faster embedding generation for your stored documents. This directly benefits vector search latency—your RAG system can generate query embeddings quickly and match them against your Milvus collection with minimal delay. Choosing Embedding Models for RAG in 2026 covers how to select embedding models that balance performance with deployment efficiency.