In this guide, we outline considerations and best practices for designing such a heterogeneous infrastructure including how to leverage different GPU models, high-speed storage, and networking to maximize performance for both training and inference workloads. WHY HETEROGENEOUS . AI model training and inference workloads are forcing the industry to rethink not only how much compute fits in a rack, but how servers are architected from end to end — transforming computing infrastructure as we know it. As with all things, one size rarely fits all. When it comes to AI infrastructure it's entirely feasibleto spin up a cluster with your GPU of choice and get. MultiCortex is a tech company that developed the world's first operating system based on heterogeneous computing. Heterogeneous computing involves the use of different types of processors (CPU, GPU, FPGA, among others) working together to enhance performance and efficiency, emerging as the future. Modern AI models are data-hungry, computation-heavy beasts that need specialized hardware just to function, let alone perform at their best. That's the job of an AI server—a custom-built system that keeps AI applications fast, scalable, and efficient. Perfect for scaling artificial intelligence fast. Use tabs to select server type. Filter by location, CPU, and RAM. or chat with us to find your. A 4 U chassis supports a maximum of eight full-height full-length dual-slot heterogeneous accelerator cards with a maximum power consumption of 350 W or 32 half-height half-length heterogeneous accelerator cards with a maximum power consumption of 75 W.