Private LLM and GPU cluster delivery, edge AI nodes, cross-border compute links, AIOps and a unified AI API gateway — built on the same network and operations team, and run 24×7.
AI workloads are network and operations problems first: GPU interconnects, edge sites and cross-border data paths. GSS extends 15 years of network delivery to these workloads and stays on to operate them.
LLM inference clusters, RAG knowledge bases and AI apps — support assistants, compliance documents, meeting notes — inside your boundary.
GPU servers, high-speed fabrics, storage and scheduling; cross-border compute links with transfer audit.
Inference at plants, campuses, stores and warehouses; video stays on site and only results move.
One gateway for domestic and international model providers, with quotas by company, department, user and key, plus cost attribution and audit.
Business goal, data boundary, model and hardware; 4–6 week PoC on cost and results.
GPU, edge nodes and links procured, integrated and isolated from production.
Data preparation, fine-tuning and integration; phased rollout with human review.
24×7 monitoring of compute, network and apps; quarterly cost and results reviews.
Moving 40 TB daily datasets from Shanghai to GPU clusters in Singapore and Tokyo took 20+ hours over the internet, and training data had to leave China over an auditable path.
A six-person local team covered GxP data rooms, offices nationwide and cloud resources; 11 monitoring tools produced 15,000 alerts a month and HQ needed English reporting and audit evidence.
MPLS between plants was costly and slow to scale, cloud ERP/MES performed poorly, and the nightly data sync with the German HQ took more than six hours.
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