Flexible GPU Infrastructure: The Hidden Advantage Behind Scientific Breakthroughs

When infrastructure can scale with demand, AI stops being a bottleneck and becomes a catalyst.

In a recent conversation with Voltage Park CEO Ozan Kaya, Radical AI Co-Founder Jorge Colindres explained why CPUs alone are no longer enough to drive discovery - especially in material sciences.

Scaling Science with GPUs

Why data & models need compute

Data and AI models require high performance compute at every stage of the scientific process. At every stage of the scientific process, Radical AI’s self-driving laboratory is demanding computational throughput during experimentation, complex model training during hypothesizing, and parallel compute as many models have to be operating in tandem.

• Training complexity: ML models must ingest and process high-dimensional data representing billions of possible material configurations.
  
  
• Throughput demands: Experimentation pipelines generate massive data streams that require real-time analysis.
  
  
• Parallelization: Running models at scale is only possible with GPU clusters designed to execute thousands of simultaneous computations.
  
  

Without infrastructure capable of meeting these demands, progress in material science cannot match the scale of the problem.

Radical AI’s Flexible Infrastructure Needs

Moving from concept to experimentation with adaptable GPUs

Radical AI’s workflow requires flexibility at every stage. Moving from design to real-world testing depends on infrastructure that adapts to changing requirements:

• Massive data streams from continuous experimentation must be ingested, processed, and looped back into model training.
  
  
• ML model training needs GPUs that can handle workloads growing in size and complexity as research accelerates.
  
  
• Robotics integration in the self-driving lab requires seamless synchronization between computational outputs and automated hardware inputs.
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Flexibility is not optional. As we discussed in this previous article about the decades-long process of hypothesizing and building semiconductors, businesses must move exponentially faster than manual speed allows.

The Voltage Park Advantage

Reliable, flexible GPU power

Radical AI leans on Voltage Park’s infrastructure because it provides the right combination of reliability, adaptability and scalability their mission demands:

• Scalable infrastructure means compute is never a limiting factor.
• Flexibility in GPU capacity matches workloads of varying sizes without delay.
• Reliability provides confidence that experiments and models can run continuously without interruption.
  
  

Voltage Park delivers the foundational hardware and software infrastructure that teams can rely on to act on their vision for scalable, repeatable material sciences discoveries.

Let one of our experts help you determine how our flexible, reliable and scalable NVIDIA AI infrastructure can advance your scientific discoveries.

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