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Savings and Efficiency: The New Era of AI with Open Source Synthetic Data

The ability to generate high-quality synthetic data has become an invaluable resource. The recent introduction of NVIDIA’s NeMo Guardrails 340B model marks a significant milestone in this area, providing a powerful and affordable tool for startups and small businesses looking to train large-scale language models (LLMs). This model, with 340 billion parameters, is one of the largest and most advanced open-source models available today, designed specifically to integrate with the NVIDIA NeMo and NVIDIA TensorRT platforms.

I was already covering in my previous post “NVIDIA Omniverse and the creation of clones of the world” related content that has a lot to do with the use of synthetic data to perform all kinds of simulations,

Source:NVIDIA
Source:NVIDIA
What is Synthetic Data?

Synthetic data are computer-generated data that replicate the properties and characteristics of real-world data .

Se are derived from existing data sets or created using advanced algorithms and models. These data cover a variety of processes and techniques, from simple data synthesis to deep learning models.

Synthetic data is a powerful tool in AI and machine learning due to its low cost and ease of production, accurate labeling, and ability to minimize bias present in real-world data.

They also enable a reduction in the need for real data, with Gartner predictions suggesting that by 2025 we will need 70% less real data to power AI . This represents de facto unprecedented savings in training these models.

Synthetic Data Generation and Evaluation: The Solving Problem

One of the main challenges in training artificial intelligence models is obtaining high quality data sets. These data are crucial to ensure the performance, accuracy, and quality of the responses of the customized models. However, obtaining such data sets can be prohibitively expensive and difficult, especially for small and medium-sized companies. This is where the NeMo Guardrails 340B model and NVIDIA’s Nemotron-4 340B family of models play a crucial role.

Source: A syntetic data generation
Cost Savings with Open Source Models

Traditionally, obtaining high quality data sets involves significant investments in time and financial resources.

Small and medium-sized companies often face prohibitive barriers in terms of cost and access to this data. By using open source models such as those developed by NVIDIA, companies can generate their own high-quality synthetic data without incurring the high costs of data acquisition.

In addition, the ability to customize these models using proprietary data allows companies to tailor the model’s capabilities to their specific needs without the need for costly development from scratch. This approach not only reduces upfront expenses but also lowers long-term operating costs, making the development of advanced AI technologies more accessible to a wide range of companies.

Launch and Comparison with GPT-4

NVIDIA’s Nemotron-4 340B compares favorably with OpenAI’s GPT-4 in both size and capabilities, excelling in different benchmarks such as GSM 8k and MMLU.

This model has generated a great deal of interest due to its very permissive open license and advanced capabilities. However, its local implementation is complicated and requires advanced hardware such as a DGX H100 with 8 GPUs, which limits its use through APIs and cloud services.

Don’t miss this video by Matthew Berman where you will see this model in action.

The introduction of these NVIDIA Opensuurce models represents a significant advance in the democratization of artificial intelligence. enabling small and medium-sized companies to access high-quality synthetic data generation technologies, reducing costs and barriers to entry.

With these models, NVIDIA not only facilitates the development of robust and accurate LLMs, but also drives open innovation by providing powerful and accessible resources for the global community of AI developers.

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