A workload-led sizing plan
Benchmark the model and task before choosing CPU/GPU capacity, memory, storage and network requirements.
AI Compute & GPU Infrastructure
Capacity that follows the workload.
We design and manage the compute environment your AI needs. From model serving to fine-tuning and batch processing.
Discuss your workloadSERVICE ILLUSTRATION · NOT A LIVE PRODUCT
What computing capacity do we need, and how can we run it efficiently?
What the engagement delivers.
A serving environment
Configure inference, queues, scheduling, shared or dedicated capacity, quotas and autoscaling where appropriate.
Measured optimisation
Review utilisation, latency and cost. Compare model optimisation, batch processing and scaling decisions against the workload.
ILLUSTRATIVE APPLICATION
Interactive answers and overnight document batches
Separate time-sensitive model requests from queued document jobs. Test the memory and throughput requirements before proposing a capacity and scheduling plan.
Connected to the right systems.
Connect serving endpoints, application queues, storage, identity and monitoring. Procurement may be arranged through suitable providers.
With the right controls.
Usage limits, tenant isolation and cost alerts bound the workload. Capacity and commercial terms depend on the selected provider and project.
Where it runs.
How it stays useful.
Inference runs a trained model to produce answers or predictions. Training and fine-tuning change model weights and need a separate workload assessment.
What shapes the scope?
Model size, context length, concurrency, availability, tuning requirements and provider charges drive the capacity plan. We do not advertise owned datacentres or guaranteed GPU inventory.
Explore your requirementsDo we need dedicated GPUs?
Not necessarily. Managed model APIs, CPU workloads or shared capacity may fit better. Start with a benchmark and the operating constraints.
Can you give us a fixed GPU price now?
A useful quote needs the workload, required capacity, deployment pattern and provider terms. Cloud or compute consumption may be separate from engineering and management fees.
What should AI do
for your business?
Tell us what you want to build, connect or improve.
We’ll help define the architecture, the delivery and what it takes to run it.