Size for the workload
Assess models, context lengths, concurrency and latency targets. Benchmark representative workloads before committing to a GPU configuration.
BUILT FOR INTELLIGENCE
Run AI workloads with the right GPU capacity.
For AI and platform teams deciding how much GPU capacity to buy or deploy. We benchmark your models against expected demand, then design the GPUs, storage, networking and inference services around your latency and budget requirements.
WHAT WE BUILD TOGETHER
Connect the full stack
Align accelerators with storage throughput, networking, drivers, scheduling and the model-serving layer. Avoid treating GPU capacity as an isolated purchase.
Make capacity operational
Plan workload isolation, resource quotas, monitoring and expansion. Track utilisation and inference performance to inform capacity decisions.
UNDERSTAND THE ENGAGEMENT
What it is.
How we deliver it.
What you are buying
AI infrastructure includes accelerators, host systems, storage, networking and the model-serving software. The right design depends on the workload: interactive inference, batch processing and training have different capacity and operational needs.
Where it is useful
A benchmark is the best starting point when a team knows its models but not the capacity it should buy. It is also useful when a prototype is slow, concurrency is unpredictable or a private inference service needs to support real staff workloads.
What a delivery scope can include
We define representative prompts, output sizes, concurrency and latency targets, then evaluate candidate configurations. The architecture covers model loading, storage throughput, scheduling, quotas, monitoring and expansion. Where Kubernetes is appropriate, GPU support and workload isolation are tested with the actual runtime.
What to bring to discovery
- Candidate models, licences and required precision
- Representative inputs, context lengths and outputs
- Peak concurrency and response targets
- Data boundaries, budget and availability requirements
How we evaluate the result
Record answer quality alongside first-response latency, completion time, throughput and memory use. Include sustained load and failure conditions. Compare cost per accepted task rather than hardware specifications or token throughput alone.
Decisions to make early
Sizing cannot be guaranteed from employee count or model parameter count. Quantisation and runtime changes affect both quality and capacity. Dedicated hardware, shared capacity and managed endpoints are compared against the actual deployment constraints.
BUILD YOUR UNDERSTANDING
Useful reading before we talk.
Private AI & RAG · 3 MIN
Explore all services ↗Is fine-tuning obsolete, or just expensive?
Understand when adapting a model still makes sense and how to compare training costs with prompting and retrieval.
Private AI & RAG · 3 MINAWS AI services: when to consider Bedrock or SageMaker AI
Compare managed foundation-model applications with custom model development, then plan data, evaluation and regional requirements.
Private AI & RAG · 3 MINHow much GPU capacity does your AI application need?
Plan inference capacity using model memory, context length, concurrency, latency and real workload benchmarks.
A LITTLE MORE CLARITY
Good questions.
Clear answers.
Do we need a dedicated GPU cluster?+
Not always. Model size, expected demand, data constraints and budget determine whether dedicated, shared or smaller on-premises capacity is appropriate.
Do you support private inference?+
We can design inference services inside your approved environment. Model availability, licensing and hardware compatibility are validated during discovery.
DISCUSS YOUR FIRST USE CASE