- Text Generation
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Nemotron 3 Nano 30B A3B
- Text Generation
Nemotron 3 Nano 30B A3B is a 30-billion-parameter NVIDIA language model variant optimized for compact deployment with efficient inference. It targets on-device or resource-constrained environments while retaining strong general-purpose text understanding and generation capabilities.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Nemotron 3 Nano 30B A3B?
Nemotron 3 Nano 30B A3B is an NVIDIA large language model with roughly 30 billion parameters designed for efficient, small-footprint deployment. It is mainly used for general-purpose natural language tasks such as chat, content generation, and code assistance in scenarios where compute or memory budgets are limited. It is also suited for edge or enterprise environments that require locally hosted AI with reduced latency and improved data control. It is part of NVIDIA’s Nemotron 3 model family, which includes multiple sizes and variants optimized for different deployment and performance needs.
Model capabilities
5 Core Capabilities
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Conversational AI
Supports multi-turn, context-aware chat and instruction following, enabling natural language assistance, explanations, and task-oriented dialogue for various domains.
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Code Generation
Generates and completes code snippets, explains programming concepts, and assists with debugging across common languages using natural language prompts.
-
Language Translation
Translates between multiple natural languages, enabling cross-lingual understanding and communication while preserving core meaning and intent.
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Document Understanding
Performs optical character recognition on textual images or scanned documents, extracting machine-readable text for downstream processing and analysis.
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Image Captioning
Generates brief textual descriptions of provided images, identifying key objects and relationships to summarize visual content.
Use cases
6 Most Valuable Use Cases
- Enterprise Q&A Assistant
- Invoice / Document Parsing
- Knowledge Base Search
- Compliance Case Monitoring
- Developer Code Assistance
- On-Device Reasoning
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for Nemotron-class 30B models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.20 | $0.20 | 128K |
| NVIDIA NIM | US East | ~150ms | ~70 tps | ~99.9% | ~$0.35 | ~$0.35 | ~64K |
| AWS Bedrock (Nemotron-equivalent 30B) | US West | ~180ms | ~55 tps | 99.9% | ~$0.40 | ~$0.40 | ~32K |
| Azure AI (Nemotron-equivalent 30B) | EU West | ~190ms | ~50 tps | 99.9% | ~$0.42 | ~$0.42 | ~32K |
| RunPod (Nemotron 3 Nano 30B A3B) | Global | ~220ms | ~40 tps | ~99.5% | ~$0.30 | ~$0.30 | ~16K |
Performance benchmarks
Technical Specifications
| Metric | Nemotron 3 Nano 30B A3B | Llama 3.1 70B Instruct | Mixtral 8x7B Instruct |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~200ms |
| Context Window | 16K | 128K | 32K |
| Input Price ($/1M) | $0.20 | $0.50 | $0.35 |
| Output Price ($/1M) | $0.40 | $1.50 | $0.70 |
| Max Output Tokens | 4K | 8K | 8K |
| Throughput | 120 tps | 90 tps | 100 tps |
| Uptime | 99.5% | 99.9% | 99.9% |
30-day usage via LLM API
- 1.8B
- Prompt tokens processed (30 days)
- 220M
- Completion tokens generated (30 days)
- 3.4M
- API requests served (30 days)
- 99.8%
- Average uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent Model Routing
Automatically route each request to the best model across providers based on latency, cost, and capability—no client changes, just smarter defaults and safer upgrades.
One endpoint, any model -
Cost-Aware Orchestration
Control spend with price-aware routing, per-project limits, and transparent usage analytics so you can tune model choices without rewriting application logic.
Optimize cost, not code -
Resilient Fallback Flows
Define automatic failover to alternate models or providers on errors, timeouts, or rate limits to keep production workloads stable under real-world conditions.
Never drop a request -
Full-Stack Observability
Trace every request across providers with logs, metrics, and structured events so you can debug prompts, tune routing, and prove reliability to stakeholders.
See every token -
Task-Centric Abstractions
Use high-level task APIs for chat, tools, RAG, and workflows so you can swap models and providers without rebuilding your application architecture.
Code to tasks, not models -
High-Throughput Batch
Run large-scale generations and evaluations in managed batches with automatic retries and concurrency controls, dramatically reducing cost and operational overhead.
Scale runs, not ops
Decision guide
When to Use — When NOT to Use
Use it if...
- You need an open, locally deployable LLM for on-premises or air‑gapped environments.
- You need to fine-tune a 30B model on domain-specific data using NVIDIA GPUs.
- Your use case involves moderate-length chatbots or assistants with solid general language abilities.
- You need to run inference efficiently on NVIDIA hardware with good CUDA and TensorRT support.
- Your use case involves prototyping LLM applications where full frontier-level intelligence is unnecessary.
- You need a commercially usable model without complex licensing constraints from third-party providers.
Avoid if...
- You need cutting-edge reasoning and problem solving comparable to the very latest frontier models.
- Your workload requires extremely long context windows for large documents or codebases.
- You need best-in-class performance on multilingual tasks far beyond high-resource languages.
- Your workload requires specialized vision, audio, or multimodal capabilities integrated in one model.
- You need guaranteed low-latency, globally distributed inference managed fully by a cloud provider.
- You need strong, battle-tested safety guardrails and content filtering out-of-the-box for consumers.
FAQ
Frequently Asked Questions
-
What is Nemotron 3 Nano 30B A3B?
Nemotron 3 Nano 30B A3B is an NVIDIA 30B-parameter language model optimized for efficient text generation and instruction-following via LLM.API.
-
What is Nemotron 3 Nano 30B A3B best suited for?
It is best for fast, low-cost text generation, code assistance, and chat-style agents where efficiency and small-footprint deployment matter.
-
What context window does Nemotron 3 Nano 30B A3B support via LLM.API?
Nemotron 3 Nano 30B A3B supports a 4,096 token context window through LLM.API.
-
How fast is Nemotron 3 Nano 30B A3B on LLM.API?
Latency is generally low and throughput high, making it suitable for real-time applications, though exact speed depends on your request size and concurrency.
-
What modalities does Nemotron 3 Nano 30B A3B support?
Nemotron 3 Nano 30B A3B is a text-only model, supporting text input and text output only.
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How is Nemotron 3 Nano 30B A3B priced on LLM.API?
Pricing is per-token for input and output and is set by LLM.API; check the Nemotron 3 Nano 30B A3B pricing table for current rates.
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How do I access Nemotron 3 Nano 30B A3B through the LLM.API?
You call the unified LLM.API endpoint with provider set to NVIDIA and model set to nemotron-3-nano-30b-a3b.
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How does Nemotron 3 Nano 30B A3B compare to similar models?
Compared to larger NVIDIA models, it trades some reasoning depth and knowledge breadth for lower latency and better cost-efficiency.
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What are the main limitations of Nemotron 3 Nano 30B A3B?
It may struggle with very complex reasoning, long multi-step tasks, or domain-expert knowledge compared to larger frontier models.
-
Can I fine-tune Nemotron 3 Nano 30B A3B via LLM.API?
Direct fine-tuning is not exposed; instead, use system prompts, instructions, and in-context examples to specialize behavior.
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