AI-Ready Data Centre
Future-Proof, High-Performance Infrastructure for AI & Traditional Workloads
Infrastructure for AI & Traditional Workloads
Modern AI workloads push data centers into uncharted territory. You face pressures like:
- Skyrocketing data volumes and throughput needs
- Ultra low-latency and high interconnect demands (east-west traffic)
- Huge power, cooling, and density constraints
- Security risks across infrastructure, models, and data
- Fragmented visibility, monitoring, and control across layers
- Balancing cost, efficiency, and scalability
If your data center isn’t built for AI, you risk performance bottlenecks, inefficient use of expensive resources, and security blind spots.
Key AI Data Centre Challenges
Massive Throughput & Interconnect Demands
AI workloads require massive data flows between GPUs, storage, and across nodes (east-west traffic). Legacy fabrics choke under the volume.
Latency & Jitter Sensitivity
Distributed training or inference tasks are extremely sensitive to latency, jitter, and microbursts. Any delay impacts model convergence or performance.
Thermal / Power / Density Constraints
High-performance computing (HPC) and GPU clusters demand huge power and cooling. Data center space and thermal budgets are limited.
Security Across Data, Models & Infrastructure
AI workloads, models, and training data are valuable and vulnerable to attacks, tampering, or theft across infrastructure.
Siloed Visibility & Management
Compute, storage, network, and AI control planes rarely align; problems that cross layers are hard to detect.
Cost, Efficiency & Scaling Pressure
The cost of overprovisioning to avoid bottlenecks is high; underprovisioning causes performance failure. Scaling incrementally is tough.
Subnetik Solutions
Massive Throughput & Interconnect Demands
Deploy next-gen switching and interconnect fabrics (400G, 800G, purpose-built silicon) with AI-optimized packet flow, buffer management, and congestion avoidance.
No more network bottlenecks — your infrastructure keeps pace with AI processing.
Latency & Jitter Sensitivity
Use per-packet load balancing, microburst detection, adaptive routing, congestion control techniques, and hardware telemetry.
Traffic moves with deterministic performance — AI pipelines stay smooth and predictable.
Thermal / Power / Density Constraints
Energy-efficient switching silicon, compact modular designs, intelligent cooling (liquid / immersion / optimized airflow), power-aware architectures.
Maximize compute density per rack without overheating or waste — future scale fits.
Security Across Data, Models & Infrastructure
Embed security in the network fabric — segmentation, workload isolation, encryption in motion, zero-trust access, model shadow detection.
You reduce risk exposure and protect high-value AI assets by design
Siloed Visibility & Management
Unified observability across network, compute, storage, and AI job layer; AI/ML-based correlation, fault detection, topology awareness.
You get root-cause insight faster and manage holistically.
Cost, Efficiency & Scaling Pressure
Pre-validated modular systems, automation, capacity planning tools, predictive scaling, and consumption-based growth models.
You right-size everything, scale linearly, and control TCO.
Why This Data Centre Approach Works
- Throughput at Scale — Infrastructure built for massive data movement, not just general compute.
- Predictable Latency & Performance — AI workloads demand consistency, and your network delivers it.
- Efficiency & Density — Smarter cooling, silicon, and design to maximize ROI per installed unit.
- Security Built-In — No afterthoughts: models, data, infrastructure all protected.
- Unified Operations & Insight — One dashboard, one control plane, one truth across layers.
- Modular, Future-Ready Design — Scale out, scale up, adapt to new AI innovations without rework.
These capabilities let you deploy AI workloads faster, more reliably, and with confidence — across data centers, edge sites, or hybrid cloud fabrics.
