Definition
A Training Cluster is a group of interconnected compute servers designed to work together for training artificial intelligence and machine learning models. Training clusters typically use multiple GPUs or other AI accelerators to distribute computationally intensive model-training workloads across multiple nodes.
A training cluster commonly includes GPU servers, high-speed networking, shared or distributed storage, power infrastructure, cooling systems, and cluster management software. High-bandwidth, low-latency interconnects are particularly important because distributed training requires frequent communication and synchronization between GPUs and compute nodes.
Training clusters are optimized primarily for AI model training and fine-tuning, rather than serving production inference workloads. As model size and training datasets grow, clusters can scale from a few GPU servers to large-scale deployments spanning multiple racks or data halls.
Related Terms
- AI Cluster — A broader computing cluster designed for AI workloads, including training, inference, and other AI applications.
- GPU Cluster — A cluster composed of multiple GPU-accelerated compute nodes; commonly used to build AI training infrastructure.
- AI Data Center — Data center infrastructure designed to support AI clusters and other high-density AI workloads.
- AI Infrastructure — The broader infrastructure stack supporting AI computing, including compute, networking, storage, power, and cooling.
Related ATTOM Solutions
Training clusters can create substantial requirements for high-density power, high-speed networking, and advanced cooling. ATTOM provides modular and prefabricated data center solutions designed to support high-density AI computing and scalable AI infrastructure deployments.
