Márton Münz, PhD

GPU-intensive AI model deployment

Large-scale generative AI models are rapidly transforming biotech by enabling powerful new capabilities in various areas including drug discovery, genomics, medical imaging, personalized medicine. In drug development, for example, AI models can accelerate target discovery, molecule design, and virtual screening.

Trained on massive datasets with deep learning techniques these neural network-based models require significant computational power not only during training but also during inference - the process of generating outputs - which can be time-intensive and typically relies on high-performance hardware called GPUs (Graphics Processing Units).

At the same time, the explosion of these large scale AI models has driven demand for on-demand GPU compute resources. The major cloud providers (AWS, GCP, Azure) as well as specialized GPU cloud providers (e.g. Modal, Lambda Labs, CoreWeave, RunPod) let users rent powerful GPUs by the hour or minute. For biotech companies, relying on such cloud-based GPU compute resources for their customized AI applications can be convenient for the following reasons:

• - Scalability and flexibility: Biotech companies can dynamically scale their compute resources up or down based on project demands, avoiding the upfront costs and inflexibility of purchasing dedicated hardware. Minute-level (or even second-level) billing and scale-to-zero eliminate wasted idle cost.
• - Access to cutting-edge infrastructure: Cloud providers offer access to the latest GPU hardware (e.g. NVIDIA A100, H100), which might otherwise be cost-prohibitive.
• - Faster experimentation cycles: researchers can quickly prototype, test, and iterate AI models without infrastructure bottlenecks, accelerating discovery timelines.
• - Operational efficiency: Managed GPU cloud services reduce the burden of system maintenance, updates, and hardware failures, allowing biotech teams to focus on scientific innovation rather than IT operations.

What I offer

I offer end-to-end support for deploying GPU-intensive AI models in biotech, helping teams build scalable, cost-effective cloud infrastructure for training and inference. From selecting the right GPU cloud provider to optimizing performance and minimizing idle costs, I ensure your AI workloads run efficiently and integrate smoothly with your scientific workflows.