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Nemotron 3 Nano 30B A3B (free)
- Text Generation
Nemotron 3 Nano 30B A3B is NVIDIA’s open-weight, 30B-parameter hybrid Mixture-of-Experts Mamba-Transformer language model optimized for efficient reasoning and long-context workloads. This free variant targets high-throughput agentic applications while remaining deployable on modern GPU infrastructure.
About the model
What is Nemotron 3 Nano 30B A3B (free)?
Nemotron 3 Nano 30B A3B is a 30-billion-parameter open-weight large language model from NVIDIA based on a hybrid Mixture-of-Experts Mamba-Transformer architecture tailored for efficient reasoning. It is designed for agentic and tool-using workflows such as code generation, math and science problem solving, and long-context analysis of documents and conversations. It is also used as the language backbone for multimodal systems like Nemotron 3 Nano Omni, supporting downstream tasks including computer-use agents and enterprise assistants. The model belongs to NVIDIA’s Nemotron 3 family (Nano, Super, Ultra), succeeding earlier Nemotron generations with a focus on open, efficient reasoning at 30B scale.
Model capabilities
5 Core Capabilities
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Conversational Chat
Handles multi-turn natural language conversations, answering questions, following instructions, and maintaining context across user interactions.
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Code Assistance
Generates and explains code snippets, helps with debugging, and provides programming guidance for common languages and libraries.
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Language Translation
Translates between major natural languages, preserving meaning and tone while producing fluent, grammatically correct output.
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Text Analysis
Summarizes, rewrites, and classifies text, extracting key information and improving clarity while retaining original intent.
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Vision Understanding
Interprets image content, identifying objects, scenes, and relationships to support multimodal reasoning and description tasks.
Use cases
6 Most Valuable Use Cases
- On-device Text Generation
- Code Autocompletion
- Chat-based Assistants
- Language Translation Support
- Edge AI Applications
- GPU Inference Optimization
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and best performance for Nemotron-scale 30B models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.02 | $0.02 | 128K |
| NVIDIA | Global | ~200ms | ~40 tps | 99.9% | $0.00 | $0.00 | ~32K |
| AWS Bedrock (Nemotron-equivalent 30B) | US East | ~220ms | ~35 tps | 99.9% | ~$0.60 | ~$0.60 | ~32K |
| Google Cloud (Nemotron-equivalent 30B) | US Central | ~210ms | ~38 tps | 99.9% | ~$0.55 | ~$0.55 | ~32K |
| Azure AI Studio (Nemotron-equivalent 30B) | EU West | ~230ms | ~30 tps | 99.9% | ~$0.65 | ~$0.65 | ~32K |
Performance benchmarks
Technical Specifications
| Metric | Nemotron 3 Nano 30B A3B (free) | Llama 3.1 8B Instruct (free) | Mistral 7B Instruct (free) |
|---|---|---|---|
| Avg Latency | ~220ms | ~250ms | ~260ms |
| Context Window | 16K | 8K | 8K |
| Input Price ($/1M) | $0.00 | $0.00 | $0.00 |
| Output Price ($/1M) | $0.00 | $0.00 | $0.00 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | ~45 tps | ~40 tps | ~38 tps |
| Uptime | 99.5% | 99.5% | 99.5% |
30-day usage via LLM API
- 2.4B
- Prompt tokens processed (last 30 days)
- 210M
- Completion tokens generated (last 30 days)
- 3.1M
- API requests served (last 30 days)
- 420K
- Unique users (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent Model Routing
Automatically route each request to the optimal model across providers based on latency, cost, and capability—without changing your integration or redeploying code.
One endpoint, many models -
Cost-Aware Orchestration
Control spend with price-based routing, hard budget guards, and granular usage controls while still accessing frontier models when they deliver meaningful value.
Lower spend, same quality -
Resilient Fallback Flows
Define automatic failover chains so requests transparently retry on backup models or providers, reducing downtime and flaky responses without application-level logic.
Always-on AI reliability -
Full-Stack Observability
Trace every call across providers with logs, metrics, and latency breakdowns so you can debug prompts, tune routing, and catch regressions in production.
See every token, everywhere -
Task-Native Abstractions
Use high-level task APIs for chat, generation, extraction, tools, and RAG so you can swap models without rewriting business logic or prompt scaffolding.
Code to tasks, not models -
High-Throughput Batch Jobs
Run large-scale batch inference with concurrency controls, retries, and progress tracking—ideal for backfills, fine-tuning prep, and bulk content generation.
Ship massive workloads fast
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a fully local, free LLM for experimentation without ongoing API costs.
- Your use case involves basic chatbots, assistants, or agents with moderate reasoning needs.
- You need on-device inference on NVIDIA GPUs where small footprint and speed matter.
- Your use case involves fine-tuning or LoRA training on a 30B-parameter open model.
- You need to prototype LLM features in an application before committing to larger models.
- Your use case involves educational or hobby projects that must avoid paid proprietary APIs.
Avoid if...
- You need cutting-edge reasoning, planning, or coding performance comparable to frontier proprietary models.
- Your workload requires extremely long context handling, such as book-length documents or transcripts.
- You need state-of-the-art multilingual understanding and generation across many low-resource languages.
- Your workload requires highly reliable safety, hallucination resistance, and enterprise-grade alignment guarantees.
- You need ultra-low-latency, high-concurrency serving for millions of users without GPU scaling complexity.
- Your workload requires specialized capabilities like high-quality vision, speech, or tool use beyond text.
FAQ
Frequently Asked Questions
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What is Nemotron 3 Nano 30B A3B (free)?
Nemotron 3 Nano 30B A3B (free) is a 30-billion-parameter NVIDIA language model optimized for efficient text generation and reasoning via LLM.API.
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What is Nemotron 3 Nano 30B A3B (free) best suited for?
It is best suited for fast, low-cost code completion, chatbots, and general-purpose text generation where latency and efficiency matter.
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How much does it cost to use Nemotron 3 Nano 30B A3B (free) on LLM.API?
Nemotron 3 Nano 30B A3B (free) is available at zero per-token cost on LLM.API, subject to fair-use and rate limits.
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What is the context window of Nemotron 3 Nano 30B A3B (free)?
Nemotron 3 Nano 30B A3B (free) supports a 4,096-token context window for combined input and output on LLM.API.
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Which modalities does Nemotron 3 Nano 30B A3B (free) support?
Nemotron 3 Nano 30B A3B (free) is a text-only model, supporting text prompts and text completions but not images, audio, or video.
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How do I call Nemotron 3 Nano 30B A3B (free) through the LLM.API?
You select the NVIDIA provider and specify the model name "nemotron-3-nano-30b-a3b-free" in your LLM.API completion or chat request.
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What latency and speed should I expect from Nemotron 3 Nano 30B A3B (free)?
As a nano-optimized 30B model, it typically returns first tokens within a few hundred milliseconds under normal LLM.API load.
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How does Nemotron 3 Nano 30B A3B (free) compare to similar 30B-class models?
It generally offers competitive quality to other 30B open models while emphasizing inference efficiency and lower cost on NVIDIA-optimized hardware.
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What are the main limitations of Nemotron 3 Nano 30B A3B (free)?
It can hallucinate facts, lacks real-time knowledge, and is less suitable for very long documents due to its 4K context window.
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Can I use Nemotron 3 Nano 30B A3B (free) for commercial applications?
Yes, commercial use is allowed through LLM.API, subject to NVIDIA’s model license and LLM.API terms of service.