Kyle Tretina

Kyle Tretina is a product marketing leader at NVIDIA, focused on advancing AI for digital biology and drug discovery. He drives the strategy and storytelling behind BioNeMo and our work with BioPharma, shaping how next-generation foundation models and GPU-accelerated microservices transform molecular and protein design. With a PhD in molecular microbiology and immunology, Kyle bridges science and strategy, translating breakthroughs in AI, chemistry, and biology into platforms that accelerate discovery for researchers, startups, and pharmaceutical companies worldwide.
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Posts by Kyle Tretina

Simulation / Modeling / Design

How to Optimize Transformer-Based Models for Low-Precision Training

Transformer architectures are the backbone of many modern large language and generative AI models. As these models grow in size, training runs consume more GPU... 9 MIN READ
Simulation / Modeling / Design

Fine-Tuning Biological Foundation Models with LoRA Using NVIDIA BioNeMo Recipes

Foundation models are reshaping computational biology. Pretrained on massive corpora of protein or genomic sequences, models such as ESM2 (a protein language... 12 MIN READ
Simulation / Modeling / Design

Scaling Biomolecular Modeling Using Context Parallelism in NVIDIA BioNeMo

For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU,... 9 MIN READ
Data Science

How to Accelerate Protein Structure Prediction at Proteome-Scale

Proteins rarely function in isolation as individual monomers. Most biological processes are governed by proteins interacting with other proteins, forming... 10 MIN READ
Agentic AI / Generative AI

Designing Protein Binders Using the Generative Model Proteina-Complexa

Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or... 10 MIN READ
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Data Science

Scale Biology Transformer Models with PyTorch and NVIDIA BioNeMo Recipes

Training models with billions or trillions of parameters demands advanced parallel computing. Researchers must decide how to combine parallelism strategies,... 7 MIN READ