Model & hardware assessment
Compare model quality, licensing, memory requirements, quantisation options, and expected concurrency against your real workload.
09 / Infrastructure
Private AI Inference & Monitoring
Deploy and operate models on infrastructure that fits your privacy, performance, and control requirements.
The opportunity
Your models. Your operating environment.
Some workloads need greater control over where data goes and how models run. We assess the model, hardware, and operating requirements together, then build local, private-cloud, or hybrid inference with the monitoring your team needs to operate it.
From scope to system
What we build with you.
A focused implementation, shaped around your systems, data, and operating requirements.
Inference deployment
Set up suitable serving engines, model loading, API endpoints, access controls, and deployment configuration on the agreed infrastructure.
Performance & capacity
Measure time to first token, throughput, queueing, and memory use. Tune batching, context limits, and caching where supported.
Monitoring & operating procedures
Monitor GPU and service health, request errors, usage, and quality signals. Document updates, capacity planning, recovery, and data flows.
An example in practice
Internal document assistance on private infrastructure.
An illustrative deployment serves a suitable open-weight model beside an internal retrieval system. The design reviews ingestion, telemetry, backups, and tool calls as well as the inference endpoint.
ILLUSTRATIVE USE CASE
01Internal application
02Private retrieval
03Local model endpoint
04Monitor & maintain
Define success early
Measure the work.
Improve the outcome.
We agree on relevant baselines and acceptance criteria before implementation. Depending on your scope, these may include:
- Task quality at the selected model size
- Time to first token and throughput
- GPU utilisation and memory headroom
- Total operating cost and recovery
Does local inference guarantee that data never leaves our network?
Not by itself. Retrieval, tools, telemetry, updates, and backups may involve external services. We map these flows and design the deployment around the data boundary you require.
Is private inference cheaper than a model API?
It depends on utilisation, hardware, model size, operational effort, and the quality you need. We compare total operating cost and performance using representative workloads.
Can we combine local models and hosted providers?
Yes. A hybrid architecture can route suitable tasks locally and use hosted models where permitted. The routing policy should enforce data sensitivity, region, and feature requirements.
From ambition to action
Your next advantage
starts here.
Bring us your requirements. We’ll help turn them into a clear scope and a practical path forward.