NVIDIA has released open-source training recipes through its BioNeMo platform, demonstrating how Low-Rank Adaptation enables efficient fine-tuning of billion-parameter biological foundation models on a single workstation GPU. The announcement addresses a persistent bottleneck in computational biology: adapting massive pretrained models for specialized tasks typically demands prohibitive compute resources and storage. By freezing the original model weights and training only lightweight adapter matrices, the approach reduces trainable parameters to approximately one percent while preserving downstream performance. The BioNeMo workflow integrates the Parameter-Efficient Fine-Tuning library, NVIDIA Transformer Engine, and Megatron-Bridge optimizations. Two primary case studies validate the methodology across distinct biological modalities. For protein modeling, researchers fine-tuned the 3-billion-parameter ESM2 language model on secondary structure prediction, a token classification task that assigns structural labels to amino acid sequences. Leveraging Transformer Engine acceleration and THD sequence packing to eliminate padding overhead, training completed in under one hour on a single NVIDIA RTX 6000 Blackwell Workstation Edition GPU. The adapted model achieved 84.80 percent Q3 and 74.30 percent Q8 accuracy, matching established state-of-the-art baselines while dramatically reducing memory and compute requirements. Parallel testing applied the same parameter-efficient methodology to genomics using the 1-billion-parameter Evo2 DNA foundation model. Evo2, which utilizes striped Hyena architectural blocks for efficient long-context processing, was adapted for splice-site classification. This sequence classification task requires identifying intron boundaries from raw nucleotide windows, a challenge complicated by recurring genomic motifs. Training only 1.42 percent of the model parameters through adapter matrices increased test accuracy from 52.3 percent to 96.6 percent, effectively recovering nearly all contextual signal retained by the pretrained backbone. The unified training pipeline proves adaptable across both protein and DNA architectures, emphasizing reproducibility and accessibility for academic and industrial researchers. NVIDIA BioNeMo Recipes package includes complete source code, configuration templates, and evaluation utilities, allowing users to replicate the results or modify target modules for new biological tasks. By demonstrating that complex foundation model adaptation no longer requires large-scale distributed clusters, the release lowers infrastructure barriers and accelerates the deployment of specialized AI in drug discovery, genomic analysis, and structural biology. All implementations remain publicly available through the BioNeMo Recipes repository.