1️⃣ Nemotron 3 Nano
Universal model for reasoning and chat, focused on local deployment.
Key characteristics:
- MoE architecture: 30B parameters total, ~3.5B active
- Context up to 1 million tokens
- Hybrid architecture:
- 23 Mamba-2 + MoE layers
- 6 attention layers
- Balance between speed and quality of reasoning
Requirements:
- About 24 GB of video memory is needed for local deployment
The model is well suited for long dialogues, document analysis, and reasoning tasks
An interesting example of how MoE and Mamba are actually starting to reduce hardware requirements while maintaining context scale and quality.
2️⃣ Nemotron 3 Super & 3️⃣ Nemotron 3 Ultra
significantly surpass Nano in scale - by about 4 times and 16 times respectively. But the key point here is not just the size of the models, but how NVIDIA managed to increase power without a proportional increase in inference cost.
NVFP4 and the new Latent Mixture of Experts architecture are used for training Super and Ultra. It allows for four times more experts to be used at the same inference cost. Essentially, the model becomes "smarter" due to a more flexible selection of experts, rather than constantly activating all parameters.
Additionally, Multi-Token Prediction is used, which accelerates training and improves the quality of reasoning on long sequences. This is particularly important for agentic and multi-agent scenarios, where models work with long context and complex decision chains.
A good signal for the industry. Release, Guide, GGUF, lmstudio
#AI #LLM #NVIDIA #Nemotron3 #OpenSource #MachineLearning
••••••••••••••••••••••••••••••••••••••
🤖 Data Science, ML & Big Data with @DataXplore