LightweightNVIDIA

Nemotron 3 Nano 30B

Nemotron 3 Nano 30B is NVIDIA's lightweight text model with a 262K token context window, optimized for speed and efficiency.

Context 262K
Tier Lightweight
Input from
$0.050 / 1M tokens
across 2 providers

API Pricing

ProviderInput / 1MOutput / 1MSpeedTTFTUpdated
$0.050$0.20093.4 t/s309ms4/4/2026
$0.050$0.20093.4 t/s309ms4/14/2026

Prices updated daily. Last check: 4/14/2026

Model Details

General

Creator
NVIDIA
Family
Nemotron
Tier
Lightweight
Context Window
262K
Modalities
Text

Capabilities

Tool Calling
No
Open Source
No

Strengths & Limitations

  • Large 262K token context window for processing lengthy documents
  • Fast inference speed at 67.46 tokens per second output rate
  • Quick response time with 303ms time to first token
  • 30B parameter size balances capability with computational efficiency
  • Optimized by NVIDIA for performance-focused deployments
  • Streamlined architecture without tool calling overhead
  • Suitable for high-throughput text processing applications
  • No multimodal support - text input and output only
  • Lacks tool calling and function execution capabilities
  • Proprietary model with no open source availability
  • Lightweight tier positioning limits complex reasoning capabilities
  • No structured output modes like JSON formatting

Key Features

262,144 token context window
Text-only input and output processing
67.46 tokens per second generation speed
303ms time to first token latency
Streaming response support
30 billion parameter architecture
NVIDIA-optimized inference performance
Lightweight model deployment profile

About Nemotron 3 Nano 30B

Nemotron 3 Nano 30B is NVIDIA's lightweight model in the Nemotron family, designed for efficient text processing tasks. As a 30 billion parameter model, it sits in the mid-range of model sizes while maintaining NVIDIA's focus on performance optimization. The model supports a 262,144 token context window and focuses exclusively on text processing without multimodal capabilities. Performance benchmarks show it generates 67.46 tokens per second with a time to first token of 303 milliseconds, indicating optimization for responsive applications. The model does not include tool calling functionality, keeping its design streamlined for core language tasks. Nemotron 3 Nano 30B targets use cases where balance between capability and efficiency matters, offering substantial context handling while maintaining faster response times compared to larger models in enterprise and production environments.

Common Use Cases

Nemotron 3 Nano 30B is well-suited for applications requiring efficient text processing with substantial context handling. Its 262K token window makes it effective for document analysis, content summarization, and text classification tasks involving lengthy inputs. The fast inference speed and quick response times support high-volume production environments, customer service applications, and real-time text processing workflows. The lightweight tier positioning makes it appropriate for scenarios where speed and throughput matter more than complex reasoning capabilities, such as content moderation, basic text generation, and batch processing of textual data.

Frequently Asked Questions

How much does Nemotron 3 Nano 30B cost per million tokens?

Nemotron 3 Nano 30B pricing varies by provider and may include different rates for input and output tokens. Check the pricing table above for current rates across all providers offering this model.

What is Nemotron 3 Nano 30B best used for?

Nemotron 3 Nano 30B excels at high-volume text processing tasks where speed matters, including document analysis, content summarization, and text classification. Its large 262K context window and fast 67.46 tokens/second output make it ideal for production environments requiring efficient processing of lengthy documents.

Does Nemotron 3 Nano 30B support tool calling or multimodal inputs?

No, Nemotron 3 Nano 30B is designed as a streamlined text-only model without tool calling capabilities or support for images, audio, or other modalities. This focused design contributes to its fast inference performance and lightweight deployment profile.