The main functional difference between general compute servers and AI-accelerated compute servers is their ability to handle artificial intelligence workloads, such as machine learning and deep learning.
AI-accelerated compute servers are typically equipped with specialized hardware, including graphics processing units (GPUs) and field-programmable gate arrays (FPGAs) that are optimized for AI workloads. They may also run specialized software frameworks like TensorFlow or PyTorch for developing and running AI models.
AI-accelerated compute servers are typically equipped with specialized hardware, including graphics processing units (GPUs) and field-programmable gate arrays (FPGAs) that are optimized for AI workloads. They may also run specialized software frameworks like TensorFlow or PyTorch for developing and running AI models.
Key challenges
AI-accelerated compute servers require extra processing power, memory, storage, network bandwidth, cooling, and security to maintain optimal performance. AI-accelerated servers are deployed in higher density systems, which is driving the demand for more efficient power delivery throughout the data center.
Our products and solutions
ST offers extensive product coverage of server needs for data centers, ranging from advanced power management solutions, including power-conversion ICs for 48 and 12 V power architectures, to microcontrollers, environmental sensors, protection devices, and other key components, such as analog functions and secure solutions.
