NVIDIA Nemotron 3 Super delivers 5x higher throughput for agentic AI

New NVIDIA Nemotron 3 Super Delivers 5x Higher Throughput for Agentic AI

NVIDIA launched Nemotron 3 Super, a 120B open model with 12B active parameters, and says it delivers up to 5x higher throughput for agentic AI. It has a 1M-token context window and uses hybrid MoE, latent MoE, and multi-token prediction; the post says Blackwell NVFP4 gives up to 4x faster inference than Hopper FP8, with over 10T training tokens disclosed. What matters is that NVIDIA is releasing open weights, training recipes, and RL environments for reproduction and fine-tuning.

Why it matters: This is a solid model-release story with all three HKR signals, led by strong HKR-K: parameter counts, active params, context length, training scale, and Blackwell/Hopper comparison are all concrete. It stays below 85 because the key performance claims come from NVIDIA's own blog