GPU infrastructure sized for your AI workload
Avoid paying for idle capacity or running short at peak. We help you plan and operate GPU infrastructure for AI inference and training, based on measured workloads.
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NVIDIA
Kubernetes
Docker
Google Cloud
OllamaWhat does GPU infrastructure for AI involve?
GPU infrastructure includes the accelerators, servers, drivers, scheduling and monitoring that run AI models. For language models, memory often sets which models fit, while concurrency and response targets determine how many GPUs you need.
The choice is not only about hardware. Serving software, quantisation and batching can change capacity needs substantially, so we test before recommending a purchase or long commitment.
Our GPU infrastructure services
Practical support for buying, renting and running GPU capacity.
Workload sizing
Measure representative traffic and test models to estimate required capacity.
Cloud or on-premises
Compare cloud GPUs, dedicated servers and owned hardware using your usage.
Serving optimisation
Tune batching and quantisation to get more from existing capacity.
Self-hosted LLMs →Kubernetes scheduling
Containerise workloads and schedule GPU resources for shared use where it suits.
GPU monitoring
Track utilisation, memory, temperature, queue time and cost per request.
Isolation and security
Separate teams and workloads, and protect model and data access.
Private deployments →Where should your GPUs run?
Cloud GPUs
Best forTesting, uneven demand and quick starts
Watch forAvailability and higher long-term cost at steady use
Dedicated servers
Best forSteady workloads wanting predictable pricing
Watch forMinimum terms and less flexibility
Owned hardware
Best forStrict data rules and long-term steady use
Watch forUpfront cost, power, cooling and maintenance
How we plan GPU infrastructure
We prove capacity with tests before a major spending decision.
Profile
Measure traffic and model behaviour.
Model
Compare options and running costs.
Pilot
Load test on rented capacity.
Deploy
Automate setup, drivers and scheduling.
Optimise
Review utilisation and scale decisions.
GPU infrastructure planning guide
Separate inference from training
Inference and training place different demands on GPU infrastructure. Inference needs steady response times and enough memory to hold the model. Training or fine-tuning runs in heavy bursts, so many teams rent capacity for it while keeping inference on dedicated resources.
Watch memory and concurrency
GPU memory determines which models fit, while concurrency and response length determine how many GPUs you need. Serving software that batches requests can substantially increase throughput, so test the full serving stack rather than estimating from hardware specifications alone.
Check the full cost
Compare more than the hourly or purchase price. Owned hardware adds power, cooling, space, spares and staff time. Cloud adds data transfer and storage. Calculate cost per completed task at your expected utilisation, because idle GPUs are the most expensive ones.
Plan for failure
GPU nodes fail and drivers change. Automate setup with containers, keep tested driver versions, and decide what happens if capacity is lost. Some systems can fall back to a smaller model or an external provider, which keeps essential services available.
Official resources: NVIDIA data center, Kubernetes GPU scheduling and vLLM documentation.
GPU infrastructure questions
How many GPUs do we need?
It depends on the model, concurrent users, response length and latency target. We run load tests on representative traffic rather than relying on a general rule.
Should we buy or rent GPUs?
Renting usually suits testing and uneven demand. Buying can make sense for steady, long-term use or strict data requirements, once power, cooling and support are included.
Can several applications share a GPU?
Often yes, with suitable scheduling and limits. Latency-sensitive services may still need reserved capacity.
Can you reduce our current GPU costs?
Often there is room to improve through model choice, quantisation, batching and better scheduling. An audit of real usage shows what is possible.
Related AI infrastructure and services
Size your GPU infrastructure
Share your model, usage and response targets, and we will build a capacity plan you can test.
Talk to our AI infrastructure team