Guangdong-Hong Kong-Macao Greater Bay Area Dedicated AI Compute Network Guide: Connecting Multi-Site Data Centres, R&D Offices, and Overseas Model Resources

Four stages where Greater Bay Area AI R&D is frequently slowed down by the network

When Greater Bay Area AI teams advance model training, fine-tuning, and application development, their networks simultaneously carry sustained high-throughput transfers of large files and latency-sensitive real-time interactive traffic. When transmitted directly over the public internet, R&D work frequently runs into bottlenecks at four stages:

• Slow and easily interrupted pulls of overseas models and datasets: When algorithm teams download open-source large model weights and datasets from platforms such as Hugging Face and ModelScope, files ranging from tens to hundreds of GB transmitted across borders over the public internet often experience sharp speed drops or break off mid-download, leaving GPUs in the data centre sitting idle while waiting for data (for ways to address slow model downloads, see our guide to reliably pulling Hugging Face large models).
• Unstable training data synchronisation across multiple locations: Many teams clean and label corpora at offices in Shenzhen or Guangzhou before transferring the data to self-built data centres in Dongguan or Foshan, or syncing fine-tuning checkpoints between cloud environments in Hong Kong or overseas and local data centres. When large files are transferred over cross-region or cross-border public internet paths for extended periods, mid-transfer packet loss and disconnections often force repeated retransmissions.
• Lag and dropped connections on remote SSH sessions and inference APIs: When engineers in the office connect via SSH to GPU containers in another city or overseas to write code and inspect logs, or when business systems call remote inference APIs, they need continuous, stable bidirectional connections. During peak-hour jitter on the public internet, terminal command typing becomes noticeably sluggish and persistent connections drop easily (if your team primarily connects to overseas cloud compute, see our network guide for domestic teams remotely accessing overseas GPU compute).
• Inconsistent speeds when accessing overseas AI services like ChatGPT, Claude, and Gemini: When R&D and product staff use overseas large model services such as Claude, ChatGPT, and Gemini for coding assistance, technical research, or API testing, direct connections over the public internet frequently encounter slow page loads, interrupted streaming responses, or API request timeouts (for office access optimisation, see our network guide for enterprises reliably accessing overseas AI services).

Common causes behind these symptoms include egress congestion, circuitous routing, packet loss, and jitter on the public internet during cross-region and cross-border transmission. At the same time, background downloads of large files in a data centre saturating local broadband, or peak-hour rate limiting on overseas AI platforms and model repositories, can produce similar lag and errors. Your team does not need to diagnose the root cause from a single occurrence on its own—we can help sort through the situation based on your actual workloads.

Connecting offices, multi-site data centres, and overseas AI resources on a single dedicated compute network

Built around the R&D pattern of Greater Bay Area enterprises operating with multi-city offices, remote compute, and cross-border resource access, we provide an SD-WAN cross-region and cross-border dedicated transmission network that links enterprise branches, data centres, AI services, and model repositories into a single network (for overall planning of enterprise compute networks and internal data centre fabrics, see our general guide to building an enterprise dedicated compute network):

• Connect over existing broadband using physical or virtual gateways: Offices and data centres across the nine Pearl River Delta municipalities (Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Dongguan, Zhongshan, Jiangmen, and Zhaoqing) as well as Hong Kong and Macao can connect to our network directly over their existing internet broadband without laying new physical dedicated lines, provisioning faster than traditional carrier dedicated lines. Offices and physical data centres can deploy preconfigured physical appliances, while public cloud or virtualised environments can deploy virtual gateways; the network supports TCP, UDP, and HTTP(S) protocols for bidirectional access across all nodes.
• Separate protection for interactive sessions and large file downloads: Everyday interactive chats, code completion, SSH debugging, and API calls are sensitive to latency, whereas pulling model weights and datasets from platforms like Hugging Face and ModelScope requires sustained high bandwidth. We can assign separate transmission priorities and paths for these two types of traffic so background large model downloads do not crowd out real-time interactive bandwidth.
• Nearby access in Hong Kong to reach overseas AI and model platforms: Teams in the Greater Bay Area can connect to the nearest PoP on our SD-WAN network in Hong Kong to access overseas AI services such as Claude, ChatGPT, and Gemini as well as model repositories over a dedicated channel, reducing latency and mid-stream interruptions caused by circuitous routing on the public internet.
• On-demand bandwidth scaling and optional high availability for critical sites: Whenever your team pulls open-source models in bulk, syncs training datasets at scale, or expands GPU nodes, bandwidth can be scaled on demand without waiting for traditional dedicated line construction. For data centres running core training jobs or primary R&D sites, we can also plan high-availability setups such as dual appliances or backup connections.

Multi-site AI access management, connection logging, and service responsibilities

Once multiple offices, self-built data centres, and cloud environments across the Greater Bay Area are brought onto the same dedicated compute network, R&D leads and management teams can also govern daily usage through network-level controls while keeping responsibilities clear across parties:

• Centrally enable or disable AI access by site and by service: Within the dedicated compute network, we support centrally enabling or disabling access to designated overseas AI services by site and by service. Enterprises can uniformly configure which R&D sites or data centres are permitted to access specific external services, without having to modify settings on each individual computer or server when policies change.
• Retain network connection logs without touching business content: The network can record DNS resolution and 5-tuple connection logs to help enterprises keep track of external connections across sites. These logs remain strictly at the network connection layer—we never decrypt or inspect the prompts or document content entered by employees.
• Division of responsibilities with third-party platforms: We are responsible for providing the dedicated transmission network across your enterprise branches, data centres, and cloud environments along with the connectivity channel to target platforms. Account registration, subscription billing, API keys, regional service policies, platform rate limits or risk controls, and platform outages on services such as ChatGPT, Claude, and Gemini are handled by the respective platforms and your enterprise; we do not provide third-party AI accounts, nor do we promise to bypass platform regional rules or risk controls.

When you begin planning a Greater Bay Area dedicated compute network, you do not need to prepare network test reports or complex topology diagrams upfront. In our initial discussion, simply tell us the cities where your teams are located, the locations of your self-built compute data centres and cloud environments, and the main bottlenecks you currently face when pulling models, transferring data, developing remotely, or accessing overseas AI services—and we will provide networking recommendations tailored to your existing broadband conditions.