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AI Model Requirements
We establish what each production model needs to run effectively.
We review model size, framework, precision, memory use, request patterns and expected outputs so infrastructure is matched to the actual workload.
AI inference solutions run trained models against new data to generate predictions, responses or decisions within live applications.
Turns models into usable services
AI inference solutions deliver outputs for assistants, analytics, computer vision, fraud detection and other live applications.
Designed for responsive processing
Accelerated compute helps reduce response times and support more simultaneous requests.
Deploys across environments
Inference can run in data centres, cloud environments, branches or edge locations.
AI inference solutions provide the compute, software and supporting infrastructure needed to run trained models within live applications and operational workflows.
The right solution helps improve response times, support more concurrent requests and place inference where data, users and business processes need it.
We assess model requirements, latency targets, deployment locations and existing infrastructure, then recommend an inference platform that balances performance, scalability, integration and operating cost.
AI inference solutions help organisations run trained models reliably within live applications, services and operational processes at the required scale and speed.
Deploying AI models at scale
Shared infrastructure supports growing model volumes, users, applications and inference requests.
Delivering real-time AI insights
Low-latency processing enables faster responses for analytics, vision, assistants and automated decisions.
Optimising GPU resource utilisation
Workload scheduling and acceleration help keep expensive compute resources productive.
Reducing inference costs
Right-sized infrastructure and efficient model serving reduce unnecessary compute, power and cloud consumption.
Supporting enterprise AI applications
Secure, resilient platforms help connect inference services with business systems and operational data.
Accelerating AI innovation
Reusable deployment processes help teams move tested models into production more quickly.
AI inference solutions support production environments where trained models must deliver reliable outputs quickly, securely and at enterprise scale.
Processes live data quickly for fraud detection, computer vision, operational alerts and other time-sensitive decisions.
Provides managed infrastructure for moving tested models into reliable, repeatable live services.
Supports growing request volumes, users and applications without rebuilding the inference environment.
Runs models that classify information, recommend actions and automate defined business workflows.
Supports assistants, search and personalisation tools that require consistent, responsive model outputs.
Applies trained models to operational and commercial data to identify likely outcomes and trends.
Getting these six areas right will help your team deliver reliable model responses without overspending on compute or creating performance and governance problems.
Define the accuracy, throughput and processing needs of each model before selecting high-performance computing infrastructure.
Match GPU type, memory and scheduling to expected request volumes using appropriately sized NVLink-connected GPU infrastructure.
Set response-time targets and confirm the supporting RDMA networking can move data quickly enough.
Plan capacity, failover and load distribution across resilient RoCE-enabled infrastructure.
Confirm inference services can connect with applications, data sources and private cloud platforms.
Control access to models and data through suitable enterprise security solutions, versioning and audit processes.
Get a clear recommendation for your network
We work with your AI, application and infrastructure teams to define how trained models need to perform in production and what the supporting environment must provide. The result is a clear view of the compute, data, integration and resilience requirements needed to deliver dependable inference services.
We review model size, framework, precision, memory use, request patterns and expected outputs so infrastructure is matched to the actual workload.
We assess GPU type, memory, server configuration, resource sharing and software compatibility to avoid performance constraints and unnecessary capacity.
We test expected concurrency, batch sizes, model loading and peak demand so design decisions are based on measurable service targets.
We review preprocessing, storage access, data movement, queueing and post-processing to identify delays outside the inference engine.
We review APIs, orchestration, monitoring, version control, deployment pipelines and support ownership to reduce implementation complexity.
We model request growth, model concurrency, load distribution, failover and recovery requirements across data centre, cloud and edge locations.
We turn the assessment findings into clear deliverables your AI, application, infrastructure and procurement teams can use to approve and deploy dependable production inference services.
A summary of model requirements, current infrastructure, performance risks, integration needs and capacity gaps.
A proposed inference architecture covering compute, model serving, data flows, resilience, monitoring and security.
Suitable GPU, server, networking and software options selected around model performance and operational requirements.
A defined list of hardware, software, licensing and support needed for accurate planning and pricing.
A phased plan covering model onboarding, integration, testing, scaling, failover and production handover.
Continued support with performance tuning, model growth, capacity expansion, upgrades, renewals and platform changes.
We’re trusted by IT teams in enterprise environments, data centres and distributed locations. Our consultants help you select and deploy AI inference solutions, with practical support across platform choice, workload compatibility, integration, licensing and lifecycle planning.
AI Inference Solutions
Tell us about your current infrastructure, operational challenges and project requirements. We’ll review compatibility, integration and support needs, then identify the most suitable route forward.
We help you compare AI inference platforms, balancing workload performance, deployment location, software compatibility, scalability, licensing and operational fit.
We match platform capabilities, infrastructure requirements and service needs to your environment, workloads and operational priorities.
We help you define suitable warranties, support coverage, subscriptions and professional services for your operating model.
We assess existing infrastructure, software, facilities, data sources and workflows to ensure each element works together effectively.
We help you plan implementation, migration, support and future upgrades across the full solution lifecycle.
Need help planning AI inference infrastructure?
Speak to our experts about selecting, deploying or optimising AI inference solutions for enterprise workloads.
If you're deploying AI inference, these categories cover the GPU compute, networking, rack infrastructure and storage capacity needed to run models reliably across central and distributed environments.
GPU-accelerated systems for running production inference workloads with the processing capacity, memory and accelerator support required by deployed models.
Browse platformsLow-latency, high-throughput networking for moving model data and inference requests between compute, storage and applications without creating avoidable bottlenecks.
Browse platformsGeneral-purpose rack infrastructure for hosting supporting applications, orchestration, databases and services alongside dedicated AI inference systems.
Browse platformsCapacity-focused servers for storing model files, inference data, logs and supporting datasets close to the systems that need to access them.
Browse platformsAI inference solutions form part of a wider accelerated computing, data centre, and enterprise data strategy.
The related solutions below connect inference platforms with the compute, storage, protection, and supporting infrastructure required for production deployment.
Modernise data centre environments across compute, storage, networking, power, cooling, and management to improve resilience, efficiency, and scalability.
Explore Data Centre Modernisation ›Purpose-built AI infrastructure combining accelerated compute, high-performance networking, storage, cooling, and platform expertise for demanding AI workloads.
Explore AI Infrastructure ›Enterprise compute platforms supporting business applications, virtualisation, private cloud, and infrastructure refresh with resilient performance.
Explore Enterprise Compute ›Enterprise storage and data protection platforms supporting resilient data access, backup, recovery, retention, and long-term capacity growth.
Explore Storage And Data Protection ›Choose an inference solution by matching it to your models, user demand, response-time requirements, data location and preferred way of managing infrastructure.
The right design gives your applications enough performance without buying capacity they may never use. We assess your workload and existing environment, then compare suitable cloud, data-centre and edge options. Book an AI inference assessment.
Cloud suits flexible demand, data-centre deployment offers greater control, and edge inference supports applications that need fast local decisions on site.
Your best option depends on connectivity, data sensitivity, usage patterns, cost predictability and the infrastructure your team can support. A blended approach may give different applications the right balance instead of forcing every workload into one location.
It will only be suitable if your models, software, acceleration and application interfaces are supported together in a reliable production environment.
Powerful hardware does not guarantee that your current model will deploy cleanly or remain supported. We validate the important software and infrastructure dependencies before selection. Book a model and platform compatibility review to reduce rework and implementation risk.
GPUs are valuable when your models or response targets need more parallel processing than your standard processors can provide efficiently.
Smaller models and lower-volume services may run well on existing compute, making GPUs an unnecessary cost and power commitment. Comparing real demand against each option helps you choose acceleration that your applications will use rather than buying around headline specifications.
The right architecture gives your team predictable application performance, clearer scaling decisions and a repeatable way to move AI services into production.
It also makes resilience, support and capacity easier to plan as usage grows. Your team avoids a collection of one-off deployments, while you gain a clearer understanding of the infrastructure and budget needed to support future demand.
Yes, we can model current and expected demand, identify likely limits and design a phased platform that avoids unnecessary capacity initially.
We compare expansion, resilience, management and lifecycle options so your environment can grow without repeated redesign. Book an AI inference planning consultation to define a practical route from initial deployment to wider production use.
