Introduction: What is NVIDIA Nemotron 3 Nano?
NVIDIA announced the Nemotron 3 family of open models—a new generation designed specifically for agentic AI systems where multiple AI agents collaborate, reason over extended contexts, and route work between specialized models. The family includes three sizes: Nano (available now), Super, and Ultra (both expected in H1 2026).
Nemotron 3 Nano is a 30B total parameter model with approximately 3B active parameters per forward pass. This sparse activation is made possible by a breakthrough hybrid Mamba-Transformer mixture-of-experts (MoE) architecture, which combines Mamba-2 layers for efficient long-context processing with grouped-query attention (GQA) transformer layers for high-accuracy reasoning.
Key Model Specifications
| Specification | Detail |
|---|---|
| Total Parameters | \~31.6B |
| Active Parameters | \~3.2B (3.6B with embeddings) |
| Architecture | Hybrid Mamba-2 + Transformer MoE |
| Context Window | Up to 1M tokens |
| Training Data | 25 trillion tokens |
| License | nvidia-open-model-license |
The model delivers up to 4x higher token throughput than Nemotron 2 Nano while reducing reasoning-token generation by as much as 60%. On the Artificial Analysis Intelligence Index v3.0, Nemotron 3 Nano achieves a leading accuracy score of 52 among similarly-sized models.
Why Infrastructure Choice Matters
When deploying Nemotron 3 Nano for production—whether for RAG pipelines, coding assistants, or multi-agent systems—infrastructure choice becomes the primary bottleneck. Developers must balance Time to First Token (TTFT) against Cost per Million Tokens while ensuring the throughput needed for real-time applications.
Nemotron 3 Nano is available through multiple inference providers including Baseten, DeepInfra, Fireworks, FriendliAI, OpenRouter, and Together AI. Based on live benchmarking data from Artificial Analysis, this report evaluates why DeepInfra currently ranks as the recommended provider, outperforming standard market benchmarks in both latency and throughput.
Best Nemotron 3 Nano API Provider: The DeepInfra Advantage
DeepInfra has optimized its inference engine to leverage Nemotron 3 Nano’s sparse MoE architecture, treating the 30B total parameter model with the agility typically reserved for much smaller dense models.
| Metric | DeepInfra Performance | Market Average (Est.) | Impact |
|---|---|---|---|
| TTFT (Latency) | 0.22s | \~0.45s | Instant feel for chatbots |
| Throughput | 380 tokens/s | \~120 tokens/s | 3x faster document generation |
| Blended Price | $0.10 / 1M | \~$0.50 / 1M | 80% cost reduction |
| Context Window | 262k | 32k – 128k | Large-scale RAG support |
Deepinfra Technical Review
1. Latency (Time to First Token)
For real-time applications like chatbots, agents, and customer support tools, TTFT is the most critical metric. It represents the time between sending a request and receiving the first visible character—the “perception of speed.”
- DeepInfra: 220ms (0.22s)
- Why it matters: A sub-300ms response time is the threshold for human-perceived “instant” interaction. DeepInfra achieves this via optimized KV-caching and efficient routing, minimizing the “cold start” feeling often associated with larger models. This near-instantaneous response makes the API feel “local” to the end-user.
2. Output Speed (Tokens per Second)
Once generation begins, throughput dictates how quickly complex answers are delivered.
- DeepInfra: 380 T/s (Median P50)
- Use Case: At this speed, the API can generate a 500-token email or summary in approximately 1.5 seconds. This makes it viable for both interactive applications and background batch processing where high volume is required. At 380 tokens per second, the model outpaces human reading speed by a significant margin, ensuring no user bottlenecks.
3. End-to-End Response Time
This metric combines latency and throughput to measure how long it takes to receive a usable chunk of data (500 tokens).
- DeepInfra: 1.54 seconds
- Analysis: Receiving a complete 500-token paragraph in roughly 1.5 seconds highlights the stability of DeepInfra’s infrastructure. This low variance is critical for maintaining SLA (Service Level Agreement) standards in enterprise applications.
4. Pricing Economics
DeepInfra utilizes an aggressive pricing strategy suitable for high-volume scaling.
| Price Type | Rate |
|---|---|
| Input | $0.06 / 1M tokens |
| Output | $0.24 / 1M tokens |
| Blended (3:1) | \~$0.10 / 1M tokens |
This pricing structure is significantly lower than hyperscaler alternatives, making it ideal for startups and enterprises running heavy RAG workflows where input tokens (context) dominate. The low input price is particularly advantageous for Retrieval-Augmented Generation applications where large contexts are fed into the model.
5. Context Window
- DeepInfra Supported Context: 262k tokens
- Model Maximum: 1M tokens
While Nemotron 3 Nano theoretically supports up to 1M tokens, DeepInfra offers a massive 262k context window. This is sufficient for processing entire books, large codebases, extensive legal documents, or long-running agent sessions in a single prompt—eliminating the need for fragmented chunking heuristics.
Frequently Asked Questions (FAQ)
Is DeepInfra the cheapest provider for NVIDIA Nemotron 3 Nano?
Yes, with a blended rate of $0.10/1M tokens, DeepInfra is currently one of the most cost-efficient providers for the Nemotron 3 series, specifically for RAG applications requiring large context windows.
What is the max context window for Nemotron 3 Nano on DeepInfra?
DeepInfra supports a 262k token context window, allowing for the processing of extensive documentation, entire code repositories, or long-form legal texts in a single prompt.
How does the 380 t/s speed compare to other models?
380 tokens per second is exceptionally fast for a model of this class. Typically, speeds over 300 t/s are reserved for smaller dense models (7B-8B) or highly quantized versions running on specialized hardware. Nemotron 3 Nano achieves this through its sparse MoE architecture, which activates only \~3B of its 30B+ parameters per forward pass.
What makes Nemotron 3 Nano different from other 30B models?
Unlike traditional dense models, Nemotron 3 Nano uses a hybrid Mamba-Transformer MoE architecture that activates only 6 of 128 experts on each forward pass. This delivers the accuracy of a larger model with the inference efficiency of a much smaller one—achieving 3.3x higher throughput than Qwen3-30B and 2.2x higher than GPT-OSS-20B on equivalent hardware.
Final Verdict: Why Choose DeepInfra?
Based on benchmarking data from Artificial Analysis, DeepInfra is the recommended API provider for NVIDIA Nemotron 3 Nano.
- Unmatched Velocity: With a median output speed of 380 tokens/s, it ensures that the 30B model performs with the agility of a much smaller model.
- Ultra-Low Latency: A 0.22s start time removes the “thinking” pause often associated with cloud-based LLMs, crossing the threshold for human-perceived instant interaction.
- Economic Viability: The pricing structure ($0.10 blended) removes cost barriers for scaling applications to millions of users.
- Massive Context: 262k token support enables large-scale RAG, document processing, and long-running agent workflows.
For developers prioritizing speed-to-cost ratio in production-grade applications, DeepInfra provides the optimal balance of speed, cost, and technical reliability for NVIDIA’s Nemotron 3 Nano.
Recommended Use Cases: Chatbots, Real-time Agents, RAG Pipelines, Code Generation, Document Summarization, and Multi-Agent Systems.