AI Compute Cluster
GPU Hardware & ComputeA group of GPU servers networked to operate as a single coordinated system, the real unit in which AI models are trained and served at scale.
Eleveight AI's cluster ties 512 B300 GPUs across 64 DGX B300 nodes into one machine, NVLink binding the GPUs inside each node and an InfiniBand XDR fabric linking the nodes, so the cluster trains as a unit rather than a room of separate servers.
Overview
An AI compute cluster pools many GPU servers into a single body of compute. Modern models are far too large to train or serve on one GPU, or even one server, so the work is spread across many nodes that hold pieces of the model and the data and coordinate continuously. What matters is that they behave as one machine: the cluster, not the individual GPU, is the true unit of AI infrastructure.
How it works
The nodes are stitched together by a two-tier fabric. Inside a node, NVLink lets a handful of GPUs share memory at bandwidths high enough that they act almost as one large processor. Between nodes, a low-latency interconnect such as InfiniBand carries the constant exchange of gradients that keeps a distributed training run in sync. A scheduling and orchestration layer sits on top, placing jobs on free capacity and handling failures without losing the run.
Why it matters
A cluster is only as fast as its slowest link. Put frontier GPUs behind a weak interconnect, and they spend their time waiting on each other rather than computing, and the expensive hardware sits idle inside its own network. This is why cluster design, the fabric, the topology, the storage feeding it, decides real-world training performance far more than a raw GPU count does.
Use cases
- Distributed training of foundation models
- Large-scale inference serving
- Multi-tenant GPU cloud provisioning
- National and enterprise AI compute programs