AI, HPC and tightly coupled storage estates need network behaviour that stays predictable as shared infrastructure grows, and Nvidia Networking gives IT teams a way to keep that control across fast-moving cluster environments. The range covers managed and centrally orchestrated switches, plus host adapters that support fast, low-latency data movement and offload where it matters.
It suits data centres and other performance-led environments that need to keep compute, storage and security traffic from becoming a bottleneck. That helps teams reduce contention, support growth without redesign and maintain clear operational control as more workloads depend on the same fabric.
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Our specialists can help you compare options and recommend the right approach.
Start with your primary requirement below. This quick guide maps common infrastructure needs to the most suitable platform, helping you move from requirement to solution without unnecessary complexity.
| If you need to… | This typically means… | View options |
|---|---|---|
| Reduce AI cluster latency | Keeping GPU-to-GPU communication predictable so training jobs complete faster and clusters stay responsive under peak load | InfiniBand Switches |
| Scale Ethernet fabric capacity | Allowing network capacity to grow alongside AI and storage demand so teams avoid bottlenecks without disruptive infrastructure changes | Ethernet Switches |
| Increase server bandwidth | Giving servers the connectivity headroom to handle AI, storage, and distributed workloads without host-side performance constraints | High-Speed Network Adapters |
| Offload infrastructure services | Moving networking and security processing off the CPU so applications and AI workloads get more compute without adding servers | Data Processing Units |
| Support compact compute pods | Delivering high-speed switching in a smaller footprint so teams can build dense compute pods without exceeding space or power budgets | Compact Switching Platforms |
| Upgrade network pressure points | Targeting specific congestion points so teams can improve performance where it matters without rebuilding the wider network | Nvidia Networking Platforms |
| Deploy AI software consistently | Giving teams validated, pre-integrated platforms so AI projects move from development to production without compatibility delays | Nvidia AI Platforms |
| Manage AI lifecycle requirements | Keeping GPU infrastructure current and supported so teams can scale AI workloads confidently without falling behind on maintenance | Support & Lifecycle Management |
Browse key platforms in this series below, or speak to our specialists for help choosing the right option for your environment.
Our Nvidia BlueField DPUs portfolio covers GPU networking and infrastructure offload for AI training and cloud data centres. Compare models and request pricing.
Browse models Our Nvidia Ethernet Switches portfolio covers low-latency leaf and spine fabric options for data centres and telecoms. Compare models and request pricing.
Browse models Our Nvidia ConnectX Network Adapters portfolio covers Ethernet offload and high-speed GPU fabrics for data centres and healthcare. Compare models and request pricing.
Browse models 0Our Nvidia InfiniBand Switches portfolio covers ultra-low-latency AI and HPC fabrics for data centres. Compare models and request pricing.
Browse modelsNvidia AI Enterprise, NIM, and AI Workbench give IT and data science teams validated, enterprise-ready platforms to deploy, serve, and manage AI workloads at scale. We help organisations implement and support these software environments so AI infrastructure remains performant, current, and straightforward to operate.
We provide end-to-end services to design, deploy, and optimise GPU-accelerated AI environments. From initial architecture through to integration and scaling, we help infrastructure teams build Nvidia environments that are aligned to workload demands and positioned to grow alongside the business.
As an authorised Nvidia partner, we help organisations maximise uptime and performance across their Nvidia estate. From technical support and software updates through to lifecycle planning, we help IT teams keep AI infrastructure reliable, current, and aligned to long-term operational requirements.
We help organisations get more from their Nvidia investments — from initial architecture through to ongoing optimisation and support. Contact our Nvidia specialists for guidance today.
Backed by decades of expertise in the IT sector, our specialists support every stage of your deployment — from initial selection through to long-term lifecycle management.
Standard data centre networking works well for many business applications and virtualised environments. Pressure usually appears when AI, HPC, storage and large-scale data workloads begin demanding more bandwidth and faster system-to-system communication.
Nvidia Networking platforms support high-performance Ethernet, InfiniBand, adapters and DPUs designed for demanding compute environments. This helps organisations move data more efficiently, reduce network bottlenecks and scale infrastructure more cleanly as workloads grow.
Ethernet remains the right fit for most enterprise networking. The difference becomes noticeable when AI training or HPC workloads rely on extremely fast communication between servers across a cluster.
Nvidia Quantum InfiniBand switches such as the QM8700, QM8790, QM9700 and QM9790 are designed for these tightly connected environments, helping large compute jobs complete faster and more consistently.
Not every AI or HPC environment needs 400Gb/s networking immediately. Many research, engineering and mid-sized AI clusters still need very low latency and high throughput without the added cost and scale of NDR infrastructure.
The QM8700 and QM8790 provide 200Gb/s HDR InfiniBand connectivity in a compact 1U design, making them a practical fit for growing AI and HPC environments where HDR performance still matches operational demand.
Embedded switch management can work well in smaller isolated clusters. The challenge comes when larger AI or HPC environments need consistent provisioning, monitoring and operational control across the whole fabric.
Externally managed switches such as the QM8790 and QM9790 are better suited to centrally managed estates where IT teams want standardised control, monitoring and lifecycle management across multiple clusters.
InfiniBand is designed for tightly coupled AI and HPC workloads, but many organisations still need high-speed Ethernet that integrates naturally with existing enterprise infrastructure.
Spectrum Ethernet switches such as the SN2010, SN2100, SN2410 and SN2700 support storage, virtualisation, AI and application environments using familiar Ethernet operations. This makes them easier to integrate into mixed enterprise estates without introducing a separate networking model.
Larger leaf switches can provide more scale than smaller environments actually require. For edge locations, compact racks and smaller compute pods, that can increase cost, power usage and unused capacity unnecessarily.
Compact switches such as the SN2010 and SN2100 give organisations modern high-speed connectivity in a smaller footprint, helping teams support storage, AI and application workloads without overbuilding the rack.
100GbE networking remains suitable for many environments. The pressure point usually comes when AI, analytics, storage and distributed compute workloads begin generating more east-west traffic than the existing network can comfortably handle.
The SN3700 supports 200GbE environments, while the SN4700 supports 400GbE aggregation for larger fabrics. Higher-speed switching helps organisations move larger volumes of data with fewer bottlenecks as compute density increases.
Moving straight to 400GbE can increase optical costs and infrastructure complexity if most of the environment still operates at 100GbE.
The SN4600C provides high-density 100GbE aggregation in a 2U platform, making it a strong fit for organisations that need large-scale 100GbE connectivity without redesigning the wider fabric around 400GbE.
Traditional server designs rely heavily on the host CPU for networking, storage and security tasks. As environments become more virtualised, distributed or AI-driven, that overhead can begin consuming resources needed for applications and workloads.
BlueField DPUs offload infrastructure services from the host CPU, helping organisations improve workload isolation, strengthen infrastructure control and free server resources for production tasks.
Full DPU offload is valuable when infrastructure services need to be isolated from application workloads. Some AI environments, however, are focused primarily on moving data between GPU servers as efficiently as possible.
BlueField-3 SuperNIC is better suited to high-speed GPU networking environments focused on AI training and accelerated compute. BlueField-3 DPU is the stronger fit where networking, storage and security services also need to be offloaded from the server.
Standard network adapters can become a limitation once servers begin handling larger AI, storage or distributed compute workloads. Higher throughput and lower latency become increasingly important as infrastructure scales.
ConnectX adapters are designed for high-performance Ethernet and InfiniBand environments. ConnectX-6 supports mature 100/200GbE deployments, ConnectX-7 expands into 400GbE environments, and ConnectX-8 is designed for very large AI infrastructures requiring up to 800Gb/s connectivity.
A full infrastructure redesign can be disruptive and expensive when the real limitation exists in only a few areas of the environment.
Nvidia Networking platforms allow organisations to upgrade specific pressure points such as rack networking, cluster interconnects, adapters or infrastructure offload. This gives IT teams a more controlled way to scale AI, storage and high-performance environments without rebuilding the wider estate before it becomes necessary.
Whether you’re planning a new deployment, upgrading existing storage, or reviewing your current environment, our specialists can help you identify and implement the right solution.
