AI-Optimised Compute, Storage & HPC Solutions
High-performance compute, storage and networking optimised for model training, inference and large-scale data processing at production scale.
Why it matters
AI workloads are not like traditional enterprise computing. Model training demands GPU acceleration, high-bandwidth interconnects and parallel storage I/O. Inference at scale requires low-latency serving across distributed systems. Large-scale analytics and scientific computing need the same infrastructure discipline. Most organisations running these workloads either overprovision traditional infrastructure hoping it will cope, or accept performance penalties that add months to project timelines and inflate compute costs.
Standard enterprise infrastructure—designed around virtualisation and traditional applications—creates bottlenecks that starve GPU clusters of data, force redundant network hops, and waste expensive compute capacity waiting for I/O. The result is models that take twice as long to train, inference that misses latency requirements, and infrastructure costs that spiral because you’re paying for headroom you can’t actually use.
SCC designs and delivers infrastructure purpose-built for AI and HPC workloads. We combine GPU-accelerated compute, high-bandwidth, low-latency storage, and advanced networking into architectures optimised for your specific workload mix. You get production-ready platforms that deliver model training in weeks not months, inference that meets latency requirements, and cost efficiency that makes the business case for AI actually work.
How it works
Step 1
Profile your workload characteristics
We understand your model types, data volumes, training frequency and inference demands. Are you training large language models, computer vision systems, or mixed workloads? Will you run batch training overnight or need interactive, on-demand inference? This shapes everything downstream.
Step 2
Design compute, storage and networking for your workload mix
Based on your profiles, we specify GPU types, counts and placement. We size storage for your data access patterns and select networking fabric that matches your throughput and latency requirements. The goal is no component becoming a bottleneck.
Step 3
Build the infrastructure with proven tooling
We deploy container orchestration (Kubernetes or Slurm depending on your preference), job scheduling, monitoring and data pipelines. All components are tested together before you take ownership.
Step 4
Optimise data ingestion and model workflows
We work with your teams to optimise how data flows from ingestion through training and into inference. Often this reveals easy wins: reordering training steps, tiering data intelligently, or changing batch sizes that significantly improve overall throughput.
Step 5
Monitor and tune for sustained performance
After production deployment, we monitor GPU utilisation, identify training bottlenecks and recommend infrastructure adjustments. As your workload mix evolves, we help right-size compute and storage to keep pace.
Insights and case studies
Partners
We partner with leading infrastructure vendors and specialised AI software providers to deliver HPC and AI platforms optimised for your specific workloads.
NVIDIA provides accelerated computing platforms, tools and algorithms that enable organisations to deploy AI, high-performance computing, deep learning and virtualisation at scale. SCC is an NVIDIA preferred partner with P0 status (one of two in the UK) and holds six validated competencies: AI, AI…
Dell Technologies provides scalable compute, storage and data protection platforms for modern hybrid environments, supporting virtualisation, analytics and AI workloads across data centre and cloud infrastructure. SCC has achieved Titanium Black partner status with Dell Technologies, the highest…
Hewlett Packard Enterprise delivers edge-to-cloud solutions spanning enterprise-grade compute, hybrid cloud through GreenLake, AI-ready infrastructure with NVIDIA integration and intelligent networking combining Aruba and Juniper. HPE’s architecture enables organisations to modernise IT…
Cisco is a global leader in networking, cybersecurity, enterprise AI platforms and collaboration technologies that securely connect organisations worldwide. SCC holds the highest Cisco accreditations available, including UK Preferred Partner status across Cloud AI, Collaboration, Networking,…
Scaling AI requires infrastructure thinking, not only more GPUs.
Most organisations overlook the infrastructure constraints that make their AI workloads slower and more expensive than they need to be. Let’s discuss your workload characteristics and what an optimised, production-ready HPC and AI platform would look like for your organisation.
FAQs
What’s the difference between AI-optimised infrastructure and traditional enterprise data centre infrastructure?
Traditional enterprise infrastructure optimises for virtualisation and general-purpose workloads. AI and HPC require fundamentally different thinking: GPU compute, high-bandwidth storage interconnects, low-latency networking, and workload orchestration designed for parallel computing. We design around those requirements from the start—GPU selection, storage fabric, network topology and job scheduling all aligned to AI and HPC workload characteristics.
How do we know if our infrastructure is the bottleneck in model training?
Most organisations run GPUs at 40-60% utilisation. If your GPUs are idle, something else is the constraint: data movement, job scheduling, or network latency. We profile your workloads and infrastructure to identify where time is actually spent. Usually, it’s storage or network, not compute—and those are fixable.
Should we use InfiniBand or Ethernet for our AI cluster?
It depends on your workload and budget. InfiniBand offers lower latency and higher throughput for tightly-coupled parallel workloads (large model training, real-time inference at scale). Ethernet is simpler and cheaper for loosely-coupled workloads (batch training, offline analytics). We help you make that decision based on your specific model types and training patterns.
How much storage do we really need for AI training?
It depends on your data volumes and training frequency. A typical pattern: working set on all-flash NVMe (hot tier), recent data on SATA SSD (warm tier), and archive on object storage (cold tier). We size each tier based on your access patterns. Most organisations find tiered storage delivers 80% of all-flash performance at 40% of the cost.
What happens as we scale from 10 GPUs to 100 or 1000?
Infrastructure that works at 10 GPUs often hits bottlenecks at scale. Job scheduling, network congestion and storage contention appear. We architect from day one for your growth trajectory, with headroom for scaling. As you grow, we monitor hotspots and recommend targeted infrastructure additions to keep cost-per-job and latency stable.