A company plans to open a new regional office and roll out AI tools to every team there. Its network provider says extra capacity, which is the amount of data the network can carry, will take months to deliver and cost far more than expected. As a result, the delay stalls the plan, leaves teams waiting for promised tools, and sends money to a gap that planning could have avoided. Understanding AI workloads, which are the tasks AI systems run, helps organizations size their network before a launch instead of after it.
Knowing what these tasks need matters because each one asks for something different. Some need large bandwidth, which is the amount of data a connection can carry at one time. Others need low latency, which is the short wait between sending and receiving data. A provider that understands both needs helps organizations improve performance, grow without delays, keep connections reliable, and stay in control. The types of tasks show where those needs come from.
What are the different types of AI workloads?
The main types of AI workloads are data preparation, model training, fine-tuning, and inference. Each type uses the network differently.Data preparation and model training make up the early stages. Data preparation means collecting and cleaning large sets of data, which needs steady bandwidth. Model training then teaches an AI model with that data. It runs on graphics processing units, or GPUs, which are chips that handle many calculations at once. These chips share data constantly, which demands high bandwidth and low latency.
Fine-tuning and inference sit closer to daily business use. Fine-tuning adjusts a finished model for one job, such as answering customer questions in a company’s own words. Inference happens when the model responds to a user request, such as a chatbot reply. These AI workloads run all day and often near users, which requires steady connections. Each type adds pressure to the network.
Why do AI workloads strain business networks?
Business networks feel strain because these workloads move far more data, and move it more often, than older applications do. According to McKinsey & Company, demand for AI-ready data center capacity, meaning computing space in facilities that house AI servers, will grow 33 percent a year on average from 2023 to 2030 in a midrange scenario. The same 2024 research expects about 70 percent of data center capacity demand to come from centers equipped for advanced AI by 2030. Each new data center needs strong network links.
Those links carry a different kind of traffic. AI workloads send huge amounts of data between servers, and sometimes between data centers. In contrast, older applications mostly send data between users and servers. A shared network struggles with this load, and congestion appears, which means too much traffic competes for limited space. Jobs take longer and costs rise because expensive GPUs sit idle waiting for data.
How does dark fiber solve these networking challenges?
Dark fiber solves these challenges by giving an organization its own unused fiber optic cable, which it lights with its own equipment. This suits AI workloads because no other customer shares the strands, and a carrier cannot cap the capacity. The organization can raise capacity by upgrading its equipment instead of renegotiating a service plan.
Control over the strands brings four benefits. Bandwidth grows when demand grows, because upgrades happen on equipment the organization controls. Latency stays low and steady because data takes a direct path. Security improves because data travels on private strands. Costs can also be easier to predict, since more capacity does not always mean a new contract.
Dark fiber comes in three forms, and each fits a different distance. Metro fiber links sites inside a city or region. Long haul fiber connects cities and countries. Last mile fiber covers the final stretch to a specific site. Teams planning for AI workloads can combine these forms to match their sites. Reaching every one of those sites depends on the provider.
What should you evaluate before choosing a fiber provider?
Before choosing a fiber provider, evaluate route coverage, reliability, scalability, and control. The list include:
- Route coverage: Check that the provider reaches your data centers, offices, and cloud access points. Ask about diverse routes, which are separate paths that keep traffic moving if one cable is cut.
- Reliability: Review the service level agreement, or SLA, the contract that states guaranteed uptime and repair times.
- Scalability: Confirm you can add capacity as demand grows and ask how long delivery takes. This keeps plans for AI workloads on schedule.
- Control: Ask who owns the fiber and whether you can connect to several data centers and carriers.
A provider that answers all four points clearly is easier to trust. In addition, its network should match your sites and growth plans.
Choosing the right fiber infrastructure partner
The right fiber infrastructure keeps performance steady as demand grows. Organizations that match their connectivity to the way AI workloads move data avoid surprise costs and can add sites without redesigning the network. Coverage, reliability, and room to scale matter more than headline speed. Seeing how a provider meets these points makes the choice clearer.
ARNet is one example of a provider that meets these points. ARNet delivers fiber infrastructure for organizations that run modern network architectures. Its dark fiber solutions include metro fiber, long haul fiber, and last mile fiber. It operates across Malaysia, Indonesia, Singapore, and Thailand, and its network coverage page shows where routes are available.
Organizations choose ARNet for reliable connectivity and consistent performance across borders. Its scalable fiber infrastructure lets teams add capacity as needs increase, and its regional coverage reduces the need to work with several carriers at once. This strong foundation prepares networks for AI workloads, growing data demands, and wider digital infrastructure growth across Southeast Asia.
About the Author
Nabila Choirunnisa, Digital Marketing Executive at ARNet
