Nvidia: introduction to Nemotron Nano 2
Nvidia launches Nemotron Nano 2, a 9B-parameter model designed for edge AI agents with high token-generation speed and a configurable thinking budget.
Context
Nemotron Nano 2 is the Nano member of the Nemotron family, built for agentic workflows and reasoning on devices with memory and latency constraints.
Quick definition
Nemotron Nano 2 is a 9B hybrid Transformer–Mamba model optimized for throughput and low-cost reasoning.
Key features
The model pairs a hybrid Transformer–Mamba backbone with a thinking budget that lets developers tune accuracy, throughput and inference cost. Highlights include up to 6x token generation over similarly sized open models and potential reasoning cost savings up to 60% when using the thinking budget.
- Model size: 9B parameters
- Architecture: Hybrid Transformer–Mamba (Mamba-2 with attention islands)
- Throughput: up to 6x versus leading open alternatives in the same class
- Thinking budget: configurable limit on internal reasoning tokens
- Availability: weights on Hugging Face, endpoint at build.nvidia.com, NIM coming soon
The problem / challenge
Agentic AI needs models that are accurate and efficient. Pure Transformer designs can hit memory and throughput limits on long-context workloads, slowing adoption on edge or constrained GPUs.
Solution / approach
Nvidia's hybrid design uses mostly Mamba-2 modules (linear-time, constant memory per token) to handle long "thinking" traces efficiently, with interleaved attention layers to maintain global content jumps. The 9B Nano 2 is produced by compressing and distilling a 12B base via pruning, architecture search, and logit-based knowledge distillation to recover accuracy.
Thinking budget (brief)
The thinking budget stops internal reasoning by using the tag, enabling predictable step times and lower token usage for tasks like customer support, RAG pipelines, and edge agents.
Implementation and availability
Weights are released under the nvidia-open-model-license on Hugging Face; try endpoints on build.nvidia.com and expect NIM for high throughput and low latency. The model fits within an A10G memory budget and supports long contexts (eg. 128k) as described by Nvidia.
Conclusion
Nemotron Nano 2 provides a practical trade-off between accuracy and throughput for edge agentic use cases: hybrid architecture, configurable thinking budget and open weights make it suitable for chatbots, analytics copilots and SLA‑sensitive agents.
FAQ
Concise answers to common questions about Nvidia Nemotron Nano 2
- What is Nvidia Nemotron Nano 2? Nemotron Nano 2 is a 9B hybrid Transformer–Mamba model optimized for reasoning and agent workflows on edge devices
- How does the thinking budget work? The thinking budget inserts a tag to stop generation of internal reasoning tokens and control latency and cost
- Where can I access the model weights? Weights are available on Hugging Face and you can try the endpoint at build.nvidia.com
- Why use a hybrid Transformer–Mamba design? Mamba modules deliver higher throughput and constant per-token memory while attention layers keep Transformer-style global jumps for accuracy
- Is Nemotron Nano 2 suitable for RTX or Jetson? Yes, it is designed for deployments on RTX/Jetson where memory and thermal limits matter
- How much can the thinking budget reduce inference cost? Nvidia reports up to a 60% reduction in reasoning costs in selected scenarios when applying the thinking budget