Solution 02
GPU Computing
GPUs are the workhorse of modern AI and scientific computing. Getting value from them depends on more than the accelerator itself: drivers, interconnect topology, storage feeds, and scheduling all decide how much of that capacity is actually used.
Scope
What it covers
Accelerated nodes
Configurations matched to the workload — training, fine-tuning, inference, rendering, or simulation.
Interconnect
High-bandwidth node-to-node networking for multi-GPU and multi-node jobs.
Scheduling
Queueing and allocation policies that keep expensive hardware busy without starving smaller jobs.
Software stack
Maintained drivers, runtimes, and container images so teams start from a known-good baseline.
Approach
What we look at first
- 01Memory per accelerator
- 02Single-node vs. multi-node scaling
- 03Data loading bottlenecks
- 04Power and cooling envelopes
Related
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Tell us about the workload. We'll help you think through the infrastructure behind it.