GPU-as-a-Service: The network’s critical role in accelerated computing

The explosion of AI has created a continuous demand for computing power. At the heart of this need sits one critical resource: GPUs. They have become the hardware of choice for AI and machine learning, particularly deep learning workloads that operate on enormous data sets.

However, as organisations race to train larger models and deliver faster inference, many are discovering that owning and operating GPU infrastructure isn’t always practical. This has driven the rapid rise of GPU-as-a-Service (GPUaaS).

But while GPUaaS removes much of the complexity around hardware ownership, it doesn’t eliminate one foundational requirement. High-performance GPUs are only as effective as the network connecting them.

What is GPU-as-a-Service?

GPU-as-a-Service is a cloud computing model that allows organisations to consume GPU resources on-demand, without purchasing or maintaining physical hardware. Instead of investing millions upfront in servers, cooling, power and specialist IT staff, companies can access GPU capacity in the cloud and pay only for what they use.

This model has become especially attractive as AI workloads scale. Training large language models, running complex simulations or delivering real-time inference requires massive parallel processing – something GPUs are uniquely suited for. Their architecture allows thousands of calculations to run simultaneously, delivering far greater performance and energy efficiency than traditional CPUs.

Why GPU demand is outpacing supply

The surge in AI adoption has created a global shortage of high-performance GPUs. Cutting-edge models require thousands of GPUs working in parallel, consuming vast amounts of power and capital. For start-ups and even large enterprises, building this infrastructure in-house is often unrealistic.

GPUaaS fills this gap by giving organisations access to advanced GPU clusters without the financial and operational burden. It allows teams to focus on developing better models rather than managing hardware, while avoiding the inefficiencies of idle compute capacity.

GPU virtualisation further enhances this model by enabling GPU resources to be shared across multiple virtual machines. This makes it possible for multiple users or workloads to access GPU acceleration simultaneously or in isolation, improving utilisation and flexibility in cloud environments.

The hidden challenge: networking at GPU scale

While GPUaaS simplifies access to compute, it places significant new demands on the network.

Interconnecting thousands of GPUs is fundamentally different from traditional CPU-based cloud workloads. Typical enterprise applications operate comfortably on 10-100 Gbps networks using TCP, whereas large-scale GPU training demands 100–400 Gbps connectivity and relies heavily on RDMA (Remote Direct Memory Access) to minimise latency and maximise throughput.

Why does this matter? Because in distributed GPU environments, models are constantly synchronising parameters and exchanging data. Any bottleneck in the network slows training, wastes expensive GPU cycles and increases costs.

For inference workloads, the challenge shifts from throughput to responsiveness. When GPUs are serving real-time applications, such as chatbots, recommendation engines or computer vision systems, latency and jitter directly impact user experience. Even small delays can degrade performance or cause users to abandon the application entirely.

Key network requirements for GPUaaS workloads

Whether you’re training models or running inference on GPU-as-a-Service platforms, the network becomes a critical performance enabler. Some of the essential requirements include:

• High bandwidth: GPU clusters generate massive east–west traffic. The network must scale in lockstep with compute to avoid congestion.
• Low latency and minimal jitter: Essential for synchronised training and real-time inference workloads.
• High performance and reliability: GPU time is expensive. Network instability translates directly into wasted compute and higher costs.
• Support for advanced protocols: Technologies like RDMA are crucial for maximising GPU efficiency at scale.
• Secure, private connectivity: Many GPUaaS deployments involve proprietary data sets and multi-cloud architectures, making secure interconnection essential.

GPUaaS only works if the network keeps up

GPU-as-a-Service is transforming how organisations access accelerated computing. It lowers barriers to entry, improves flexibility and makes cutting-edge AI workloads economically viable. But it doesn’t remove the need for a robust network – if anything, it raises the stakes.

That’s why running GPUaaS over a Tier 1 backbone that is automated like PCCW Global’s, is critical. The right connectivity foundation enables organisations to fully unlock the value of accelerated compute, delivering benefits such as:

• Predictable performance and resilience: A Tier 1 backbone provides the global reach, stability and reliability required to support large-scale, distributed AI workloads.
• On-demand connectivity: Through our Network-as-a-Service platform, Console Connect, businesses can instantly connect GPU resources to clouds and data centres as needs evolve, without long provisioning cycles.
• Access to a broad partner ecosystem: Businesses can seamlessly connect to a wide range of trusted GPUaaS providers, including the leading hyperscalers as well as Scaleway and BytePlus, enabling them to choose the right platform for each workload.
• Freedom from vendor lock-in: An open, multi-provider approach gives organisations the flexibility to move between solution providers as their AI strategies change.

Ultimately, with fewer hops, lower latency and greater control over traffic flows, our network helps ensure AI applications perform as intended.