Architecture & Performance - NeMo Data Designer

Data Designer is an orchestration framework that coordinates synthetic data generation workflows. It is a client of LLM inference servers—it does not host models itself. This guide explains the architecture, execution model, and how to tune performance for your specific use case. Dataset Builder This describes Data Designer's current DatasetBuilder, which generates columns sequentially within batches. Other dataset generation strategies are in development. Data Designer processes datasets in batches, with parallel operations within each batch. Step 1: Split into batches Your dataset is divided into batches of buffer_size records. Each batch is processed completely before moving to the next. Step 2: Process columns sequentially Within a batch, columns are generated one at a time following the dependency graph. The order depends on column dependencies—expression columns may come before LLM columns if the LLM columns depend on them. Example workflow: Step 3: Generate cells in parallel Wit

Data Designer is an orchestration framework that coordinates synthetic data generation workflows. It is a client of LLM inference servers—it does not host models itself. This guide explains the architecture, execution model, and how to tune performance for your specific use case. Dataset Builder This describes Data Designer's current DatasetBuilder, which generates columns sequentially within batches. Other dataset generation strategies are in development. Data Designer processes datasets in batches, with parallel operations within each batch. Step 1: Split into batches Your dataset is divided