NVIDIA API Pricing
AI infrastructure leader with optimized inference and specialized models
6 paid models · 9 free · Price range: $0.04 - $1.20 /1M
About NVIDIA
NVIDIA, the leader in AI hardware, also offers AI models optimized for their GPU infrastructure. Their Nemotron series and partnerships provide models fine-tuned for maximum performance on NVIDIA hardware. NVIDIA's NIM (NVIDIA Inference Microservices) enables efficient deployment.
Key Highlights
- Optimized for NVIDIA GPU infrastructure
- Nemotron series models
- NIM for efficient deployment
- Strong enterprise partnerships
- Hardware-software co-optimization
Pricing Features
- Pay-per-token billing
Available through NVIDIA AI Enterprise and cloud partners. Optimized inference reduces cost per token on NVIDIA hardware.
API Features
Common Use Cases
- • NVIDIA GPU Deployments
- • Enterprise AI
- • High-Performance Inference
- • On-Premise Solutions
NVIDIA API billing guide
Focused notes for developers comparing official pricing, API docs, token billing, and model fit.
NVIDIA API billing for hosted AI models
NVIDIA API billing searches usually come from teams comparing hosted GPU-optimized inference with direct model-provider APIs. Use this page to check the NVIDIA models tracked by AI Pricing Hub, compare token prices where available, and decide whether the NVIDIA route fits your deployment and latency requirements.
- Use NVIDIA-hosted APIs when you want access to GPU-optimized inference and Nemotron-family models.
- Compare input and output token prices with non-NVIDIA routes before committing production traffic.
- Check whether your workload needs hosted API billing, self-hosted NIM deployment, or an enterprise agreement.
- Use the calculator link to translate per-token pricing into monthly spend for your expected request volume.
How to estimate NVIDIA inference cost
Treat NVIDIA model pricing as one part of the total deployment decision. Token cost matters for hosted APIs, while self-hosted NIM or enterprise deployments may also depend on GPU utilization, reserved infrastructure, support level, and throughput targets.
- Separate real-time chat traffic from batch inference because latency requirements change the best option.
- Estimate prompt tokens, completion tokens, retries, and safety or routing overhead separately.
- Benchmark response quality and throughput before moving a large workload from another provider.
NVIDIA versus direct provider APIs
NVIDIA can be attractive when infrastructure alignment and optimized inference are more important than using the cheapest general API. If your team already standardizes on NVIDIA GPUs, NIM, or enterprise AI tooling, the operational fit may outweigh small token-price differences.
- Compare NVIDIA options with OpenAI, Anthropic, Google, DeepSeek, and open-weight hosting routes.
- Prioritize NVIDIA when deployment control, GPU optimization, and enterprise integration matter.
- Choose a simpler direct provider API when you only need a mainstream chat model at the lowest setup cost.
| Best fit for NVIDIA | Use another provider when | Billing checks before launch |
|---|---|---|
| Enterprise teams already using NVIDIA AI Enterprise, NIM, or GPU-backed inference stacks. | You only need the lowest raw token price for a generic chatbot. | Confirm whether the model is billed as a hosted API, marketplace endpoint, or enterprise deployment. |
| Applications that need optimized hosted inference for Nemotron or other NVIDIA-served models. | Your product does not require NVIDIA-specific deployment, support, or infrastructure alignment. | Estimate monthly input tokens, output tokens, retries, and peak concurrency before comparing alternatives. |
| Teams comparing API billing with self-hosted GPU deployment economics. | You cannot benchmark quality, latency, and throughput before sending production traffic. | Check official NVIDIA or partner billing pages for current terms before procurement. |
Related search terms
📊 NVIDIA Model Comparison
Compare all models side by side. Sorted by total price (input + output).
| Model | Tier | Input /1M | Output /1M | Total /1M | Context | Best For |
|---|---|---|---|---|---|---|
| Nemotron Nano 9B V2 | Budget | $0.04 | $0.16 | $0.20 | 131k | Complex reasoning, math |
| Nemotron 3 Nano 30B A3B | Budget | $0.05 | $0.20 | $0.25 | 262k | Complex reasoning, math |
| Nemotron 3 Nano 30B A3B | Budget | $0.06 | $0.24 | $0.30 | 262k | Complex reasoning, math |
| Llama 3.3 Nemotron Super 49B V1.5 | Budget | $0.10 | $0.40 | $0.50 | 131k | Complex reasoning, math |
| Nemotron Nano 12B 2 VL | Balanced | $0.20 | $0.60 | $0.80 | 131k | Complex reasoning, math |
| Llama 3.1 Nemotron 70B Instruct | Flagship | $1.20 | $1.20 | $2.40 | 131k | General tasks |
🎯 Which NVIDIA Model Should You Choose?
Quick recommendations based on your use case.
💰 NVIDIA Monthly Cost Examples
Estimated monthly costs for common use cases.
| Use Case | Monthly Usage | Nemotron Nano 9B V2 (Budget) |
Llama 3.1 Nemotron Ultra 253B v1 (Flagship) |
|---|---|---|---|
|
Customer Service Bot
1000 conversations/day
|
500k input 200k output |
$0.05/mo | $0.00/mo |
|
Code Assistant
200 requests/day
|
1.0M input 500k output |
$0.12/mo | $0.00/mo |
|
Data Analysis
500 analyses/day
|
2.0M input 300k output |
$0.13/mo | $0.00/mo |
⚔️ NVIDIA vs Competitors
How does {brand} compare to other major AI providers?
| Brand | Model | Input /1M | Output /1M | Total /1M | Context | vs {brand} |
|---|---|---|---|---|---|---|
|
NVIDIA
|
Llama 3.1 Nemotron Ultra 253B v1 Current | Free | Free | Free | 131k | — |
OpenAI
|
Codex Mini | $1.50 | $6.00 | $7.50 | 200k | Infinity% more |
|
OpenAI
|
GPT-5.2 Pro | $21.00 | $168.00 | $189.00 | 400k | Infinity% more |
|
OpenAI
|
GPT-5.2 | $1.75 | $14.00 | $15.75 | 400k | Infinity% more |
|
OpenAI
|
GPT-5.1-Codex-Max | $1.25 | $10.00 | $11.25 | 400k | Infinity% more |
|
OpenAI
|
GPT-5.1 | $1.25 | $10.00 | $11.25 | 400k | Infinity% more |
|
OpenAI
|
GPT-5.1-Codex | $1.25 | $10.00 | $11.25 | 400k | Infinity% more |
All Models
❓ NVIDIA Pricing FAQ
What is the cheapest NVIDIA model?
The cheapest NVIDIA model is Nemotron Nano 9B V2 at $0.20 per 1M tokens (input + output combined).
What is the maximum context length for NVIDIA models?
NVIDIA models support up to 262k context length, allowing you to process large documents and maintain long conversations.
How do I choose between NVIDIA models?
For budget projects, choose the cheapest model. For code generation, prioritize low output price. For complex reasoning, choose models with reasoning capability. Use our scenario guide above.
How does NVIDIA API billing differ from ordinary LLM API pricing?
For hosted models, you can compare token prices like other APIs. For NIM, enterprise, or GPU-backed deployments, total cost may also depend on infrastructure, throughput, reserved capacity, and support terms.
When should I choose NVIDIA for AI inference?
Choose NVIDIA when GPU optimization, enterprise deployment control, Nemotron access, or alignment with an existing NVIDIA stack matters. If you only need a simple low-cost chat API, compare direct providers first.
Can I estimate NVIDIA API cost from this page?
Yes. Use the tracked model prices and calculator links for token-based estimates, then verify final billing terms on the official NVIDIA or provider billing page before production use.