How Tamarind Bio is Building the Inference Layer for Agentic Biology with NVIDIA

Tamarind Bio, building AI infrastructure for biotech R&D, led by Deniz Kavi, Sherry Liu, and the team, is integrating NVIDIA-optimized NIM microservices into the inference layer that powers the Tamarind platform.

Building the Infrastructure for Agentic Biology.

Advances in AI are rapidly transforming how new molecules, drug candidates, diagnostics, and biological tools are discovered and designed. Yet computational biology workflows often remain complex, requiring researchers to manage models, compute infrastructure, and reproducible pipelines before scientific work can begin.

Tamarind Bio was founded to remove that friction by providing a standardized platform and API that enables scientists to access state-of-the-art computational biology tools without managing the underlying infrastructure. The company’s vision is to help biopharma R&D organizations become agent-ready as AI systems increasingly take on end-to-end scientific workflows.

Agentic biology depends on specialized models capable of performing tasks such as protein folding, molecular design, and binder scoring. Tamarind Bio serves as the infrastructure layer connecting scientists and AI agents to these tools, while technologies such as NVIDIA BioNeMo and NVIDIA NIMs provide optimized, scalable access to the GPU-intensive models that power modern computational biology.

How Tamarind Is Using BioNeMo

Tamarind Bio is integrating NVIDIA-optimized NIM microservices into the inference layer of its platform to improve the speed and efficiency of scientific AI workloads.

The integration also expands support for agentic workflows. Through Tamarind’s MCP server and standardized API framework, NIM-based models can be incorporated into AI agents and large language model applications across a wide range of molecular biology use cases.

The architecture is designed for flexible deployment, allowing organizations to run models within the Tamarind platform or in customer-hosted environments. These capabilities can be integrated with existing research infrastructure, including ELNs, LIMS, and internal model deployment workflows.

Faster Discovery Through Efficient Inference.

As AI agents take on increasingly complex scientific workflows, access to fast and reliable specialized models becomes critical. Many computational biology tools, such as biomolecular structure prediction models, have traditionally introduced long feedback cycles that can slow scientific progress.

NVIDIA BioNeMo helps address this challenge through optimized inference infrastructure, enabling Tamarind Bio to support large-scale scientific workloads involving tens of thousands of model inference calls. Faster inference is particularly important for agentic systems, where each result influences the next step in a chain of scientific decisions. Reducing latency can significantly accelerate the overall pace of discovery across multi-step research campaigns.

As a launch partner for NVIDIA BioNeMo Agent Toolkit, Tamarind Bio is integrating NIM microservices to provide scientists and AI agents with streamlined access to leading biomolecular models through a single platform. The goal is to make frontier computational biology tools faster, more accessible, and easier to deploy without requiring researchers to manage the underlying GPU infrastructure.