GPU Cluster

A GPU cluster is a collection of servers, each equipped with one or more GPUs, connected by high-speed networking and managed as a unified compute resource. GPU clusters range in size from a few nodes in an on-premises rack to thousands of nodes in hyperscale datacenters. They provide the scalable compute foundation for AI training, inference serving, scientific simulation, and other GPU-accelerated workloads.

The architecture of a GPU cluster involves several interconnected layers. At the node level, each server contains CPUs, system memory, storage, and one or more GPUs connected via PCIe or NVLink. At the network level, nodes are connected by high-bandwidth, low-latency fabrics such as InfiniBand or RoCE for GPU-to-GPU communication across nodes. At the software level, cluster management software handles resource allocation, job scheduling, monitoring, and fault tolerance.

For AI inference, GPU clusters enable horizontal scaling across multiple GPU nodes to handle high traffic volumes. An inference workload is replicated across many GPUs, with a load balancer distributing incoming requests. The cluster can scale the number of active replicas up or down based on demand. This architecture allows a single model to handle thousands of concurrent requests by parallelizing across many GPUs, each processing a subset of the traffic.

Managing GPU clusters effectively is a significant operational challenge. Teams must handle hardware provisioning and maintenance, driver and firmware updates, network configuration, storage management, workload scheduling, resource accounting, and security. Tools like Kubernetes with GPU operators, SLURM for HPC workloads, and platforms like Cumulus OS simplify cluster management by providing higher-level abstractions for these operational concerns.

The decision between building an on-premises GPU cluster and using cloud GPU services depends on scale, utilization patterns, compliance requirements, and financial model. On-premises clusters offer lower per-GPU-hour costs at high sustained utilization and full control over data locality. Cloud GPU services offer elasticity, zero capital expenditure, and freedom from hardware management. Hybrid approaches, such as using Cumulus OS for on-premises clusters with cloud spillover, combine the advantages of both models.