Optimizing Enterprise AI Agents with NVIDIA’s Data Flywheel Blueprint for Lower Costs and Faster Performance

AI Agents and the NVIDIA AI Blueprint for Building Data Flywheels

AI agents powered by large language models are changing how enterprises manage workflows. However, their high inference costs and latency often limit scalability and user experience. To tackle these challenges, NVIDIA introduced the NVIDIA AI Blueprint for Building Data Flywheels. This enterprise-ready workflow automates experimentation to find efficient models that cut inference costs while improving latency and effectiveness.

At its core, the blueprint features a self-improving loop that leverages NVIDIA NeMo and NIM microservices. These tools help distill, fine-tune, and evaluate smaller models using real production data. The Data Flywheel Blueprint integrates smoothly with your existing AI infrastructure, supporting multi-cloud, on-premises, and edge environments.

Steps to Implement the Data Flywheel Blueprint

This hands-on demo guides you through optimizing models for a virtual customer service agent that performs function and tool-calling. It shows how to replace a large Llama-3.3-70b model with a much smaller Llama-3.2-1b model without losing accuracy—while reducing inference costs by over 98%.

• Initial setup
  Use NVIDIA Launchable to quickly spin up GPU compute resources. Deploy NeMo microservices for model customization and evaluation loops. Use NIM microservices to serve models via APIs. Clone the Data Flywheel Blueprint GitHub repository to get started.
• Ingest and curate logs
  Collect production agent interactions in an OpenAI-compatible format. Store these logs in Elasticsearch. Set up the built-in flywheel orchestrator to tag, deduplicate, curate task-specific datasets, and run continuous experiments.
• Experiment with existing and newer models
  Run evaluations using zero-shot, in-context learning, and fine-tuned setups. Fine-tune smaller models using production outputs and LoRA, eliminating the need for manual labeling. Measure accuracy and performance by integrating with tools like MLflow. Choose models that meet or exceed the original baseline.
• Deploy and improve continuously
  Review generated evaluation reports. Deploy the most efficient models into production. Continuously ingest new production data, retrain models, and repeat the flywheel cycle to keep improving through automated experimentation.

To get started, watch the new how-to video or download the blueprint from the NVIDIA API Catalog.