Recommendation research at scale needs serious compute — graph models, Transformers, repeated runs and ablation experiments add up fast. PWB Research uses high-performance GPU resources, including H200 and B300-class servers, to run work that would be inefficient on standard development machines.
They stop because larger experiments are slow, expensive or hard to reproduce. Our compute lets us run broader comparisons, repeat key experiments and test larger graph and hybrid configurations — with the discipline that makes results trustworthy.
| Compute capability | Use in recommendation research |
|---|---|
| H200-class GPU resources | Transformer training, repeated runs, larger-batch experiments and high-throughput model evaluation. |
| B300-class GPU resources | High-memory and high-throughput experiments, including larger graph and hybrid model workloads. |
| Cloud & rented GPU servers | Flexible scaling for intensive project phases and controlled cost management. |
| Future data centre roadmap | Long-term support for private AI research environments, proprietary datasets and continuous model development. |
Beyond rented and managed GPU resources, PWB Research is planning dedicated data centre capability to support larger-scale training, controlled data governance, long-running experiments and proprietary AI infrastructure.
Bigger graph and hybrid experiments without queue pressure.
Repeatable pipelines and proprietary dataset handling.
More stable GPU capacity for long-running research.
A pathway toward company-owned research infrastructure.
Graph neural networks, Transformer architectures, large parameter sweeps and long-running comparison jobs.
Discuss your workload →