NVIDIA has released a comprehensive suite of GPU-accelerated tools within its BioNeMo Agent Toolkit, significantly advancing end-to-end biomolecular structure prediction and protein co-folding capabilities. The update targets the computational bottlenecks that have long constrained high-throughput drug discovery and the modeling of large molecular assemblies. By leveraging NVIDIA H100 and Blackwell B300 architectures, the new toolkit delivers unprecedented speed and memory efficiency, enabling researchers to deploy open-source models like OpenFold3 at unprecedented scales. Traditional co-folding workflows are hindered by CPU-bound multiple sequence alignment steps and severe GPU memory limitations. NVIDIA addresses the first bottleneck with MMseqs2-GPU, integrated into the MSA Search NIM. This acceleration shifts homology search directly to the GPU, delivering up to 177 times faster alignment than legacy CPU methods while scaling efficiently across sequences exceeding ten thousand tokens. The optimization, backed by specific Hopper and Blackwell enhancements, has been upstreamed to the main open-source repository, benefiting the broader research community. For the inference stage, NVIDIA’s cuEquivariance library provides optimized geometric learning primitives that dramatically accelerate core computational kernels. When applied to OpenFold3 on B300 GPUs, cuEquivariance reduces forward-pass latency by up to three times compared to standard PyTorch implementations. Coupled with the specialized OpenFold3 NIM, which adds further inference optimizations, the pipeline achieves up to a fourfold speed improvement on Blackwell hardware. These enhancements also expand the maximum viable sequence length from approximately two thousand to six thousand four hundred tokens on a single device. To overcome the fundamental memory ceiling that restricts single-GPU co-folding to small protein complexes, NVIDIA introduced Fold-CP, a context parallelism framework. This architecture distributes computational workloads across multiple GPUs, reducing per-device memory requirements proportionally to the number of devices used. Utilizing the Boltz-2 model across a cluster of sixty-four B300 GPUs, Fold-CP successfully processes complexes containing thirty-two thousand tokens, representing a twelvefold increase over previous single-GPU limits. These technical advancements translate directly into transformative applications for structural biology and pharmaceutical research. Faster inference enables structure-based virtual screening to be deployed against compound libraries containing millions of molecules, shifting high-accuracy structural modeling from late-stage validation to early candidate discovery. Furthermore, the multi-GPU scaling framework renders previously intractable biological assemblies, such as ribosomes and spliceosomes, computationally feasible. By embedding these accelerations into a unified agent toolkit, NVIDIA ensures that AI-driven workflows can autonomously orchestrate high-performance computing tasks, bridging the gap between computational prediction and real-world biological insight. The toolkit is now available for immediate integration into agentic discovery pipelines, marking a significant step forward in AI-accelerated life sciences.