Enterprise Networking Guide
How Enterprises Can Build a Dedicated Compute Network Connecting Multi-Site GPU Nodes and R&D Teams
When building AI compute centres or renting GPU capacity across multiple locations, enterprises often face slow open-source model pulls from overseas platforms, interrupted cross-region training data syncs, and lag when remote or overseas teams connect to compute nodes via SSH and APIs. We help enterprises build a cross-region dedicated compute network over existing broadband to connect compute data centres, cloud environments, and users, balancing large data throughput, real-time remote development, and data centre access security.
After building AI compute nodes or renting GPU clusters across multiple locations, many enterprises quickly encounter a practical bottleneck: GPU computation and local storage read/write speeds inside the data centre are fast, but network connections between R&D teams, business systems, and compute nodes repeatedly slow down progress. Pulling open-source large models and datasets from overseas platforms such as Hugging Face frequently drops mid-transfer, leaving multi-hundred-GB downloads unfinished after a full day; synchronising training samples or fine-tuning checkpoints across data centres and cloud environments takes hours; and when remote or overseas colleagues connect to containers via SSH to write code or call online inference APIs, peak-hour congestion on the public internet causes lag and dropped sessions.
To smoothly connect data sources, compute data centres, and users distributed across different cities, cloud platforms, or countries, best-effort transmission over the public internet rarely satisfies both bulk file throughput and real-time interactive responsiveness. Using existing public internet broadband at each location, we help enterprises plan and build a cross-region dedicated compute network connecting compute nodes and users without laying new traditional physical dedicated lines—keeping model pulls, cross-region data syncs, remote development, and external compute services running reliably.
When planning a compute network, first distinguish intra-data-centre interconnect from a cross-region dedicated network
In recent years, international and cross-region bandwidth demand has shifted from traditional city-to-city links towards compute-node-to-compute-node interconnectivity, making connections between data-centre-dense regions increasingly critical. For enterprises, AI model distribution, cross-region training data synchronisation, and inference services make everyday corporate traffic behave more like compute infrastructure traffic, where best-effort public internet transmission struggles to deliver both bulk file throughput and low interactive latency at the same time.
Before planning your network, it helps to separate two layers of "compute networks" that are often conflated and clarify what each layer handles:
- High-speed compute network inside the data centre: Inside a single AI compute centre or data centre, the high-speed interconnect network linking multiple GPU servers for parallel training (commonly InfiniBand or RoCE networks) handles intra-rack and inter-rack GPU communication. This belongs to the internal data centre compute network and is managed by the data centre builder or compute infrastructure provider.
- Cross-region and cross-border dedicated compute network: When an enterprise needs to connect a compute data centre with remote R&D offices, compute nodes in other cities, public cloud environments, overseas open-source model platforms, and external customers, it requires a cross-region dedicated transmission and access network. We provide this cross-region and cross-border dedicated compute network transmission and access service (we do not rent out GPU compute hardware, make commitments on the computational speed of model training or inference itself, or replace InfiniBand or RoCE high-speed compute networks inside the data centre).
When R&D teams report that "jobs run slowly" or "container connections lag," the cause may be congestion and packet loss on the cross-region public internet, or it may involve heavy internal storage read/write load, platform rate limiting, or local office network fluctuations. You do not need to diagnose the root cause from a single ping test or surface symptom before reaching out—during our consultation, we can review the operational symptoms with you to pinpoint where the bottleneck lies.
Which types of connections an effective enterprise dedicated compute network needs to coordinate
When enterprises run R&D and service delivery around GPU compute, their networks carry several traffic types simultaneously, each with distinct bandwidth and latency requirements. Mixing all of them directly over standard public internet broadband leads to bandwidth contention during peak hours. When building a dedicated compute network for an enterprise, we coordinate the following connection types within a unified plan:
- High-speed pulls of open-source models and datasets from overseas platforms: When compute data centres download open-source large model weights, public datasets, and container images from overseas platforms such as Hugging Face and GitHub, traffic travels reliably over our cross-border transmission network; in measurements from our production projects, download throughput reached hundreds of Mbps (actual speeds vary with local data centre broadband and origin server response at the time and are not guaranteed as a fixed figure), reducing the time GPUs sit idle waiting for downloads.
- Large file transfers across multi-site compute nodes, cloud environments, and offices: Enterprises often preprocess data in an office or on the cloud before uploading large training datasets to an AI compute data centre, and then transfer tens or hundreds of GB of fine-tuning checkpoints and result files back after training completes. A stable cross-region transmission channel prevents repeated re-uploads of large files caused by mid-transfer packet loss and disconnects on the public internet.
- Nearest PoP access and real-time interaction for remote and overseas users: Whether internal algorithm engineers distributed across different countries and regions or external customers renting compute and calling APIs, users can connect to the nearest PoP locally and reach the compute data centre reliably over our cross-region transmission channel. The channel supports TCP, UDP, and HTTP(S) protocols simultaneously, carrying bidirectional web console access, SSH remote development and debugging, online inference API calls, and large file uploads/downloads so bulk transfers do not cause command typing and API calls to stutter.
How to connect quickly over existing broadband and manage data centre access security
When planning a dedicated compute network, many enterprises worry both that installing traditional inter-provincial or cross-border physical dedicated lines will take too long and delay project schedules, and that opening external connectivity could expose high-value models and data inside the data centre. For practical deployment and management, we support enterprises in two ways:
- Connect over existing public internet broadband and scale up bandwidth on demand: Your compute data centres, R&D offices, and cloud environments can connect to our cross-region and cross-border transmission channels directly over existing public internet broadband without applying for construction of new physical dedicated lines, cutting provisioning lead times compared with traditional carrier dedicated lines. As GPU nodes expand, R&D teams grow, or data synchronisation volumes rise, transmission bandwidth can be scaled up on demand without waiting for dedicated line construction cycles.
- Restrict exposed ports, retain connection logs, and integrate with zero-trust access: To protect valuable model weights and training datasets inside the data centre, we configure port restriction on the access devices to publish only required web consoles, API gateways, or SSH jump ports externally while isolating internal management and storage networks. Access devices also support logging DNS queries and 5-tuple connection records for daily retention and audit review; if your enterprise already uses a third-party zero-trust product, its connector can be deployed on our access devices to enforce identity-based access control for compute environments.
Planning your solution around business needs and confirming commercial details
Planning an enterprise dedicated compute network does not require preparing exhaustive network topology diagrams upfront or running complex packet captures and stress tests on your own. For an initial consultation, simply let us know which cities host your compute data centres and cloud environments, which regions your R&D engineers or external clients connect from, and what daily workloads you run, and we will help map out a suitable access architecture.
Because physical distances and existing data centre network conditions vary across cities, countries, and regions, specific network performance metrics, SLA terms, and corresponding pricing are confirmed item by item based on each site's current network conditions and formal proposal quote. Once connected, your team can directly verify the network improvement through everyday operations such as pulling open-source models, synchronising training datasets, typing remote SSH commands, and calling inference APIs.
Basic information to prepare for your initial consultation
- The cities and cloud environments where your compute data centres, AI compute centres, or cloud GPU clusters are located, and which cities, countries, or regions your R&D offices or external clients connecting to the compute nodes are mainly distributed across.
- The main workloads currently handled by the compute nodes (such as large model training and fine-tuning, daily remote development and debugging over SSH, cross-region dataset synchronisation, or providing online inference APIs and web consoles to external users).
- Specific symptoms currently hurting efficiency the most (such as slow model pulls from Hugging Face, frequent interruptions during cross-region large file transfers, or laggy command typing when connecting remotely to containers) along with approximate existing broadband and security requirements at your data centre; you do not need to prepare professional test reports or complete network topology diagrams upfront, as specific details can be confirmed step by step during the consultation.