NVIDIA Unveils Nemotron-4 340B: The Open-Source Powerhouse for Synthetic Data

The model also excels in complex software engineering tasks. On the SWE-bench benchmark, it scores 48.3%, indicating a strong ability to resolve real-world GitHub issues. Compared to the previous Nemotron-3 Super model, there is a 15% improvement in reasoning capabilities. These metrics confirm that the 340B parameter count translates directly into tangible performance gains for enterprise-grade workloads.

• MMLU Score: 86.5
• HumanEval Score: 92.1%
• SWE-bench Score: 48.3%
• Improvement vs Nemotron-3: 15%

API Pricing

For teams looking to integrate Nemotron-4 340B via API, NVIDIA offers a competitive pricing structure designed for high-volume usage. The cost is calculated per million tokens, ensuring predictability for large-scale deployments. NVIDIA also provides a free tier for developers to test the model's capabilities before committing to a paid plan, making it accessible for experimentation and prototyping.

The pricing model is structured to favor output-heavy applications, which is typical for chat interfaces and code generation. By offering a lower input cost, NVIDIA encourages users to send complex queries that the model can then process efficiently. This pricing strategy aligns with the model's strength in reasoning and generation, providing a cost-effective solution for businesses scaling their AI infrastructure.

• Free Tier: Available for testing
• Input Cost: $0.000003 per million tokens
• Output Cost: $0.000006 per million tokens
• Billing: Per million tokens

Comparison Table

To understand where Nemotron-4 340B stands in the current market, we compared it against leading open and closed models. The data reveals that while some competitors offer larger context windows, Nemotron-4 340B provides a superior balance of cost, performance, and open licensing. This makes it the preferred choice for organizations requiring transparency and control over their AI data pipelines.

The table below summarizes the key metrics. Developers should note that while closed models may offer higher raw scores on specific benchmarks, the open-source nature of Nemotron-4 340B allows for fine-tuning and customization that proprietary models cannot match. This flexibility is often more valuable for long-term enterprise strategy than a marginal increase in benchmark scores.

• Open Source: Yes
• Enterprise License: Permissive
• Customization: Fully Fine-Tunable

Use Cases

The versatility of Nemotron-4 340B makes it suitable for a wide array of applications. In the realm of coding, it serves as an excellent pair programmer, capable of generating, debugging, and refactoring code across multiple languages. For data teams, its synthetic data generation capabilities allow for the creation of realistic training datasets to improve smaller models without the need for expensive human annotation.

Beyond coding, the model is ideal for building autonomous agents and complex RAG systems. Its ability to maintain context over long conversations makes it perfect for customer support bots and knowledge management systems. Additionally, its reasoning capabilities support advanced analytics and decision-making tools, allowing businesses to automate complex workflows with higher accuracy.

• Coding & Software Engineering
• Synthetic Data Generation
• Autonomous Agents
• RAG Systems

Getting Started

Accessing Nemotron-4 340B is straightforward for developers. NVIDIA provides a dedicated API endpoint integrated into their AI platform, allowing for seamless integration with existing applications. SDKs are available for Python and JavaScript, simplifying the connection process for web and backend services. The documentation includes comprehensive examples for both inference and fine-tuning workflows.

For those who prefer self-hosting, the model weights are available on NVIDIA's GitHub repository. This allows teams to deploy the model on their own infrastructure, ensuring data privacy and compliance with internal security policies. Whether using the cloud API or on-premise deployment, the setup process is designed to be efficient and developer-friendly.

• API Endpoint: NVIDIA AI Platform
• SDKs: Python, JavaScript
• Hosting: GitHub Weights
• Docs: NVIDIA Developer Portal

Comparison

API Pricing — Input: $0.000003 / Output: $0.000006 / Context: 1M tokens

Sources