Hetao Shenzhen-Hong Kong Science and Technology Innovation Co-operation Zone Dedicated AI Compute Network: Guide to Shenzhen-Hong Kong Compute Interconnect and Overseas AI Access

From pulling models to remote training: where Hetao AI teams get bogged down on the network

Every step in an algorithm team's daily workflow is closely linked—from preparing foundation models and syncing training samples to debugging code remotely and calling overseas AI tools—and each step depends on stable cross-region and cross-border connectivity. When teams rely directly on the public internet, R&D workflows frequently run into bottlenecks at four stages:

• Slow and easily interrupted pulls of open-source model weights and datasets: When engineers download large model weights or public datasets from platforms such as Hugging Face and ModelScope, single transfers involve very large files. Crossing borders over the public internet often runs into international egress congestion and packet loss, causing download speeds to fluctuate wildly or connections to drop right before completion—leaving rented or self-built GPU compute sitting idle while waiting for data (for ways to address slow model pulls, see our guide to resolving slow and interrupted Hugging Face large model downloads).
• Lag and dropped sessions when connecting via SSH to GPU machines in Shenzhen, Hong Kong, or the cloud: When algorithm engineers connect via SSH or remote development terminals to GPU servers in the Shenzhen park, the Hong Kong park, or on a public cloud to write code and view real-time training output, they need continuous, responsive interactive sessions. As soon as the public internet path experiences jitter or consecutive packet loss, typing commands in the terminal becomes sluggish, and dropped persistent connections force engineers to log back in and restore their development environment.
• Unstable training data synchronisation between the Shenzhen and Hong Kong parks and cloud environments: Many teams clean and label data in the Futian park in Shenzhen before sending training samples to server rooms in the Lok Ma Chau Loop in Hong Kong or to cloud compute nodes for training, and then transfer fine-tuned weights back for validation once training finishes. Although the Shenzhen and Hong Kong parks sit just across the river from each other, transferring large files across the border or across clouds over standard broadband is still prone to repeated retransmissions due to circuitous public internet routing and congestion.
• Inconsistent experience accessing overseas AI services like ChatGPT, Claude, and Gemini: When researchers use Claude, ChatGPT, or Gemini in a browser to look up technical documentation or call overseas AI APIs from their projects, they frequently run into failed page loads, long responses that freeze mid-generation, or repeated API timeout errors (for more details on cross-border access scenarios, see our guide for Hong Kong and cross-border teams accessing overseas AI services).

In many cases, such lag and disconnections stem from cross-border link congestion, packet loss, and jitter on the public internet, or from someone in the office pulling massive model files at full speed and saturating local broadband. At the same time, peak-hour queuing and rate limiting, API quota throttling, or server-side outages on the overseas AI platforms and model repositories themselves can produce similar errors. When issues arise, teams do not need to jump to conclusions from a single test or run packet captures on their own before reaching out.

How a dedicated compute network is deployed across the Shenzhen and Hong Kong parks and cloud environments

Built around the practical scenarios of Hetao R&D teams collaborating across Shenzhen and Hong Kong and accessing overseas AI resources, we provide an SD-WAN cross-region and cross-border Dedicated Compute Network solution—where SD-WAN builds a dedicated transmission network on top of your team's existing broadband to connect enterprise branches, data centres, AI services, and model repositories while routing low-latency and high-bandwidth paths by workload. If your team is also planning compute data centre interconnection across cities and regions, see our Enterprise Dedicated Compute Network Guide.

Taking advantage of the adjacent location of the Hetao Shenzhen and Hong Kong parks across the river, teams can connect to the nearest PoP (point of presence) on our SD-WAN network in Hong Kong to smoothly access AI services such as Claude, ChatGPT, and Gemini as well as model platforms like Hugging Face over a dedicated channel. At the same time, this dedicated network links offices in the Shenzhen park, R&D sites in the Hong Kong park, and GPU compute nodes on public clouds into a single fabric. Whether engineers work in the Shenzhen park or the Hong Kong park, they can bidirectionally access server rooms on both sides and cloud compute environments over the dedicated network, with full support for TCP, UDP, and HTTP(S) protocols covering remote terminal development, web console access, API calls, and large dataset synchronisation.

For deployment, teams connect directly using the existing internet broadband at their offices, server rooms, and cloud environments in Shenzhen and Hong Kong without laying new cross-border physical dedicated lines, provisioning faster than traditional carrier dedicated lines. Offices and local server rooms simply deploy our preconfigured physical appliances while public cloud or virtualised environments deploy virtual gateways, allowing teams without dedicated network operations staff to get online smoothly; as the R&D team grows or training data volumes expand, network bandwidth can be scaled on demand at any time.

To prevent different R&D activities from competing for the same channel, we support traffic routing and prioritisation across the network. Everyday interactive chats, code completion, and API calls are sensitive to latency, whereas pulling model weights and datasets from platforms like Hugging Face and ModelScope requires sustained high bandwidth; assigning separate transmission priorities ensures that pulling large model files in the background does not interfere with colleagues writing code or calling online APIs.

Overseas AI access control, connection logging, and division of service responsibilities

Once the Shenzhen and Hong Kong parks and cloud environments are connected to the dedicated compute network, algorithm team leads want engineers to work smoothly while keeping external service access well governed. Our network supports centrally enabling or disabling access to designated overseas AI services within the network by site and by service, allowing teams to uniformly configure which sites or cloud environments are permitted to connect to specific AI services based on R&D roles without adjusting individual computers one by one. Meanwhile, the network can record DNS resolution and 5-tuple connection logs so teams have visibility into network connection records across sites; these logs remain strictly at the network connection layer without decrypting or inspecting the prompts or documents entered by employees.

Regarding service responsibilities, we are responsible for providing dedicated network connectivity and transport services across the Shenzhen and Hong Kong parks, cloud environments, and 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.

If you are planning a dedicated compute network for your Hetao team, you do not need to prepare network test reports or complex topology diagrams for our initial consultation—simply let us know your team's locations in the Shenzhen park or the Hong Kong park, how your GPU compute and cloud environments are distributed, and which stages of your daily R&D workflow get bogged down most often, and we will outline an access plan tailored to your existing network conditions. If your operations also involve multi-city compute collaboration across the Pearl River Delta or joint university research between Shenzhen and Hong Kong, see our Guangdong-Hong Kong-Macao Greater Bay Area Dedicated AI Compute Network Guide and Hetao Shenzhen-Hong Kong Research Network Guide.