GPU infrastructure
We help teams plan and set up infrastructure for AI workloads. The starting point is your model, expected usage and budget, followed by a workload test before agreeing performance targets.
Performance depends on the model, hardware, input size and concurrent usage. We benchmark the agreed workload before setting targets, rather than quoting general figures.
We compare rental and ownership costs using your expected usage, hardware requirements and support needs, and recommend the option that fits, which for many teams is rented capacity to begin with.
What this can include
- Workload assessment and sizing
- Cloud GPU setup and cost controls
- Inference serving setup
- Benchmarking on your workload
- Rental versus ownership comparison
- Documentation and handover
About gpu infrastructure.
Cloud GPUs or our own hardware?
We compare rental and ownership costs using your expected usage, hardware requirements and support needs. For most teams, renting is the sensible starting point.
What performance can we expect?
Performance depends on the model, hardware, input size and concurrent usage. We benchmark the agreed workload before setting targets.
What would you like to make easier?
Tell us a little about your business and the problem you want to solve. We’ll get back to you to discuss what makes sense.