Ultra-fast 9B parameter model featuring hybrid reasoning, low VRAM footprint, and optimized deployment for robotics and edge PCs.
Technical Specifications
- Provider
- NVIDIA
- Category
- open-source
- Release Date
- 2025-08
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- nemotron-nano-9b-v2
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.08/1M in) across full 131K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.02/1M tokens
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 6 GB VRAM (4-bit Quantized) · 19 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Nemotron Nano 9B v2 Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $14.00/mo vs $20 flat plan).
Break-even point: ~1,667 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Nemotron Nano 9B v2:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 32.5%
- GPQA Diamond
- 48%
- MMLU-Pro
- 71%
- HumanEval
- 80%
- Terminal-Bench
- 28%
- AIME
- 54%
- Long-Context Reasoning
- 68%
- Chatbot Arena Elo
- 1245pts
Strengths
- Blistering 140 tokens/sec throughput with tiny sub-16GB memory requirement
- Integrated hybrid reasoning mode for step-by-step problem solving
- Generous 131K context window with TensorRT-LLM acceleration
Limitations & Trade-offs
- Cannot match 70B+ architectures on complex multi-hop deduction
- Text-only without native image perception
Editorial Assessment
An astonishingly quick 9B powerhouse setting the pace for edge computing.
Best for: Robotics control, local PC copilots, and real-time interactive voice agents
Compare Nemotron Nano 9B v2
Frequently Asked Questions about Nemotron Nano 9B v2
How much does the Nemotron Nano 9B v2 API cost per 1M tokens?+
Nemotron Nano 9B v2 by NVIDIA is priced at $0.08 per 1M input tokens and $0.2 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.02 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Nemotron Nano 9B v2 pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Nemotron Nano 9B v2's context window?+
Nemotron Nano 9B v2 features a 131K tokens context window, which equals approximately 98,304 English words or ~262 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Nemotron Nano 9B v2 locally on my own GPU (Open Weights)?+
Yes, Nemotron Nano 9B v2 (9B) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does NVIDIA train on data sent to the Nemotron Nano 9B v2 API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Nemotron Nano 9B v2 perform on coding and reasoning benchmarks?+
Nemotron Nano 9B v2 achieves a composite Intelligence Index of 42/100, scoring 32.5% on SWE-bench Verified, 48% on GPQA Diamond, 71% on MMLU-Pro, and 1245 pts on Chatbot Arena Elo.
What is Nemotron Nano 9B v2 best used for?+
Nemotron Nano 9B v2 is best suited for: Robotics control, local PC copilots, and real-time interactive voice agents. Assessment: An astonishingly quick 9B powerhouse setting the pace for edge computing.
NVIDIA Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to NVIDIA's flagship reasoning tier:
Nemotron 3 Ultra 550B A55B
Nemotron 3.5 Content Safety
Nemotron 3 Nano Omni 30B A3B Reasoning
Nemotron 3 Content Safety
Nemotron 3 Super 120B A12B
Nemotron VoiceChat
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Nemotron Nano 9B v2:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.