Four GPU-heavy AI workloads reveal different infrastructure needs
Four AI workloads, from factory-floor computer vision to quantum computing simulation, are placing distinct demands on GPU infrastructure, according to research discussed by Vultr and 451 Research by S&P Global. The findings show that choosing the right setup depends on factors including location, memory bandwidth, cost and cluster density.
- Edge computer vision requires compute close to the plant floor.
- Battery image analysis can depend heavily on memory bandwidth.
- GPU-as-a-service helps firms rent capacity for intermittent training.
- Quantum simulation favors dense GPU clusters and fast interconnects.
Computer vision needs compute close to the action
The use cases come from the Agentic AI Matures research project, in which 451 Research by S&P Global interviewed 16 AI leaders across several industries. The interviews covered traditional machine learning, generative AI, LLMs and agentic workflows, with a smaller group of workloads standing out for their need for large-scale parallel processing.
One aerospace and machinery manufacturer is deploying computer vision at the edge. Cameras run AI inference to identify quality defects and safety violations as they happen, with alerts required within seconds. Vultr says the infrastructure must therefore be located close to the physical site. Sending raw video to a distant data center can add enough latency to make real-time alerts impractical, while processing close to the work can reduce the round trip.
The company CIO said GPUs may be needed as smart manufacturing creates more edge computing requirements for activities such as computer vision on the plant floor.
Large images and model training create different pressures
An advanced materials company is using machine learning to analyze high-resolution images of electric vehicle battery cross-sections. Each image is 5 gigabytes, and the company says GPU use is required for this analysis. In this case, Vultr identifies memory bandwidth as a central constraint, since large tensors can require accelerators with high-throughput memory to keep data movement from slowing the workload.
The research also describes a financial regulatory technology firm that uses GPUs for batch processing and model training. Its director said the company cannot afford to keep a large GPU cluster in its data center, so it combines GPU-as-a-service providers and rents capacity for a few hours a week. The relevant infrastructure criteria are cost per GPU-hour and access to reserved or on-demand clusters without the capital commitment of ownership.
Quantum simulation prioritizes performance
The fourth use case is quantum computing simulation on classical hardware. A chief information officer at a large defense and technology organization described building a framework for quantum computing and seeking an information-return speedup of at least 10 times compared with normal sequential processing.
Vultr characterizes quantum simulation as one of the most GPU-intensive workloads, where raw GPU density and high-bandwidth connections between nodes can matter more than cost. Across the four examples, the infrastructure requirements differ: proximity for computer vision, memory bandwidth for research imaging, low cost per GPU-hour for intermittent training and batch work, and cluster density for simulation.
The organizations interviewed are often combining services from multiple providers because, according to Vultr, no single platform has fully met all their needs. The report also says infrastructure is only part of achieving results. Clean data, disciplined model training, reliability controls, a defined business outcome and production processes are part of sustaining performance at scale.
Vultr: Four critical AI use cases built with GPUs
