GPU Cluster
GPU Hardware & ComputeA group of GPUs connected by a high-speed interconnect so they can work together on a single workload, sharing it across many chips at once.
Inside Eleveight AI's AI factory, NVLink connects the eight NVIDIA B300 GPUs in each HGX B300 system, and a high-speed network links our 64 systems, so a single training job can span hundreds of GPUs.
Overview
A GPU cluster is the accelerator layer itself: the GPUs, and the interconnect that ties them together, considered as a unit. It is a narrower idea than an AI compute cluster, which includes the full system: servers, CPUs, storage, and networking around it. A GPU cluster is specifically about how the GPUs talk to each other and share work, since that link is what determines whether hundreds of chips can actually train one model together, or just sit in the same room.
How it works
GPUs are connected through dedicated high-bandwidth links, technologies like NVLink or NVSwitch within a server, and InfiniBand or high-speed Ethernet between servers, so they can exchange data with minimal delay. During training, GPUs constantly synchronize partial results with each other, so the speed of this interconnect, not just the number of GPUs, sets the ceiling on how well the cluster scales.
Why it matters
Adding more GPUs only helps if they can communicate fast enough to work together. A GPU cluster with a weak interconnect wastes compute waiting on data transfer between chips, while a well-built one lets performance scale close to linearly as GPUs are added. This is why interconnect design is treated as seriously as the GPUs themselves in any large training deployment.
Use cases
Use cases
- Scaling model training across hundreds of GPUs
- Minimizing communication overhead in distributed training
- Sizing interconnect bandwidth for large model workloads
- Building the accelerator layer beneath a broader compute cluster