Nemotron Nano 2 9B: Open Reasoning Model with 6x Throughput for Edge and Enterprise

Overview

NVIDIA Nemotron Nano 2 9B is an open model in the Nemotron family designed for enterprise grade reasoning and agentic AI. It combines a Hybrid Transformer–Mamba backbone with a configurable thinking budget to balance accuracy, throughput and cost, making it suitable for edge and PC footprints while preserving strong reasoning capabilities. The model is released with open weights, open datasets and training techniques to support the open‑source community, and it targets reasoning across math, coding, science, tool use and general instruction following. Nemotron Nano 2 is built to fit within common edge GPU memory limits and deliver low latency thinking for agent workflows.

Key features

Hybrid Transformer–Mamba backbone designed for long thinking traces
Majority of layers are Mamba‑2 selective state‑space modules with linear time and constant memory per token
Interleaved attention islands preserve Transformer strength for linking distant facts
128k context window for long context reasoning
6x higher throughput versus the next best open model
Configurable thinking budget to control how much internal reasoning the model does
Post‑training process including supervised fine‑tuning on reasoning on/off data, reinforcement learning and preference optimization
Model compression and distillation from a 12B base to a 9B Nano 2 using pruning and logit‑based distillation
Open weights, open datasets and training techniques via NVIDIA open science initiative
Reasoning modes: Reasoning ON with chain-of-thought tokens, and Reasoning OFF for direct responses
The thinking budget can reduce token generation and potentially lower inference costs by up to 60%
The model is designed to fit within the A10G memory limits and to run with 128k context inference

Common use cases

Edge and PC/edge footprint deployments where latency matters
Enterprise‑grade reasoning and agentic AI workflows
Multistep problem solving across math, coding, science, tool use and safety
Tool calling and RAG lookups where memory and throughput matter
Long context reasoning tasks that require sustained thinking without growing memory usage
Scenarios requiring configurable accuracy and cost through the thinking budget

Setup & installation

The source describes spinning up a vLLM server for Nemotron Nano 2 and experimenting with a thinking budget. It notes that the model will be available to download and deploy via NVIDIA NIM in the future, and that NVIDIA provides open weights, open datasets and training techniques to support the open‑source community. Specific installation commands are not included in the provided material; refer to the technical report for detailed setup steps.

# Not provided in the source excerpt
# See official technical report for detailed setup steps

Quick start

A minimal, runnable quick start is not provided in the source; the article discusses a vLLM server setup and a thinking budget example. See the technical report for concrete steps and examples.

Pros and cons

Pros

Leading accuracy in its size class across reasoning tasks
High throughput enabling low‑latency agent steps
Open weights and data to support community experimentation
Flexible thinking budget to right‑size accuracy and cost Cons
Requires careful memory budgeting and hardware support (A10G memory limits described)
Complex compression and distillation pipeline (teacher–student setup)
Tuning the thinking budget for different domains may require experimentation

Alternatives (brief comparisons)

12B base Nemotron model is used as the teacher for distillation to obtain the 9B Nano 2; the 12B base consumes about 22.9 GiB of memory for weights (bfloat16)
The Nano 2 9B is designed to fit within the A10G memory limit with a target budget of about 19.66 GiB and a 5% buffer
Other open models in the Nemotron family aim to balance accuracy and throughput; Nano 2 claims 6x throughput advantage over the next best open model | Model | Parameters | Context | Throughput note | Memory / budget | Notes |---|---:|---:|---:|---:|---| | Nemotron Nano 2 9B | 9B | 128k | 6x higher than next best open model | 19.66 GiB budget; 5% buffer; 1.3 GiB for vision encoder | Open weights, datasets and training techniques; post‑training and distillation used |Nemotron 12B base (teacher) | 12B | 128k | — | 22.9 GiB for weights (bfloat16) | Used as teacher for distillation to Nano 2; larger memory footprint |

Pricing or License

The post emphasizes open weights, open datasets and training techniques as part of NVIDIA open science. No pricing details are provided in the material.