From procurement to a production environment
When deploying private LLMs and GPU clusters, enterprise failure points rarely stem from compute procurement alone. Instead, teams hit deep engineering bottlenecks: “GPU servers can be procured, but lossless fabrics fail to tune, site power and cooling hit hard walls, and post-launch operations lack hardware-level 7×24 coverage.”
GSS delivers AI infrastructure at the facility, network and operations layers. Scope: facility power and cooling assessment; server, network and storage integration; high-speed fabrics; deployment and acceptance; cross-border connectivity; monitoring and hardware operations. Not promised by default: model quality, business ROI, fixed PoC duration, long-term hardware availability. Partnership boundary: where models, data engineering or applications come from the customer or ecosystem partners, GSS owns the infrastructure, connectivity, integration and operations they run on.
| Stage | Common on-site bottleneck | GSS approach |
|---|
| Compute and facility | High-density GPU thermal and power spikes exceed standard data-room specs | Site-tailored options: retrofit-free cascaded units, professional air-cooled servers, or China-based GPU platforms, with burn-in testing |
| Fabric and transit | Packet drops over RoCEv2 degrade cluster throughput; cross-border synchronisation stalls | 200G RoCEv2 / IB lossless fabric tuning, paired with IEPL circuits and data-transfer audit gateways |
| Systems and integration | Compute islands isolated from enterprise networks | Network, storage and identity integration of compute with existing systems inside your perimeter; the application layer is owned by the customer or partner |
| Ongoing operations | No GPU-level observability for silent failures | Active-active NOCs in Beijing and Shanghai, 24/7, ingesting DCGM metrics for hardware alerts with response per the contracted tier |
Reliable AI workloads depend on resilient network and infrastructure design.
