For the past few years, AI compute has meant training. Thousands of GPUs wired into a single cluster, running for weeks to produce one model. That phase shaped how the whole industry thinks about capacity. Our view at Qovane is that the centre of gravity is shifting: inference is overtaking training as the main consumer of compute.
Why now
Training is a one-off investment; inference is an ongoing cost. Once a model is deployed in a product, every user interaction generates inference requests. As more companies wire large models into customer service, search, coding, design and content production, inference volume grows with the business — and often faster.
Newer models also tend to "think" for several steps before answering. The compute behind a single call is growing, and it is no longer a rounding error.
How inference demand is different
Training and inference ask very different things of infrastructure:
- Training values scale and continuity: large clusters, fast interconnect, long stable runs that can be scheduled in advance.
- Inference values elasticity and latency: traffic rises and falls with the business, capacity must sit close to users and scale quickly, and cost per call matters enormously.
- Training buyers are relatively concentrated; inference buyers are spread across every industry, from small teams to large enterprises.
The "big and centralised" procurement model built for training doesn't fully fit the inference era. Companies need capacity they can start and stop, pay for by actual use, and place across regions.
Three recommendations
For companies planning AI deployments, we suggest rethinking compute strategy in three ways:
- Budget training and inference separately. Their load curves differ, and so do the right buying models. Mixing them creates waste.
- Decide on utilisation, not unit price. Volatile inference loads often favour on-demand pricing; a steady baseline suits reserved capacity.
- Factor in latency and location. Inference endpoints should sit as close to users as possible while meeting local data and compliance rules.
How Qovane is positioned
The inference era calls for a network that can schedule capacity flexibly, not a single isolated facility. Through supply partnerships with data centers in several regions, Qovane brings scattered GPU capacity into one pool and offers on-demand instances billed per GPU-hour, reserved clusters for steady traffic, and ready-to-use inference endpoints and generation APIs — all priced in US dollars and delivered under service contracts.
The next phase of AI competition won't only be about who trains the largest model. It will be about who can deliver model capabilities to every user, continuously, at lower cost and consistent quality. That is the problem Qovane sets out to solve for its customers.