Model Optimization

: Overparameterization, Generalizability, and SAM The dramatic success of recent deep learning — especially within the domains of Computer Vision and Natural Language Processing — is built on “overparameterized” models: models with good enough parameters to memorize the training data...

is a to Optimizing Data Transfer in AI/ML Workloads where we demonstrated using NVIDIA Nsight™ Systems (nsys) in studying and solving the common data-loading bottleneck — occurrences where the GPU idles while it waits for input...

a , a deep learning model is executed on a dedicated GPU accelerator using input data batches it receives from a CPU host. Ideally, the GPU — the dearer resource — needs to...

Retrieval-Augmented Generation (RAG) is a robust technique that enhances language models by incorporating external information retrieval mechanisms. While standard RAG implementations improve response relevance, they often struggle in complex retrieval scenarios. This text explores...

Superb-tuning large language models (LLMs) like Llama 3 involves adapting a pre-trained model to specific tasks using a domain-specific dataset. This process leverages the model's pre-existing knowledge, making it efficient and cost-effective in comparison...