What is RDMA?

RDMA, or Remote Direct Memory Access, transfers data directly between the memory of connected systems with minimal CPU or operating system involvement. It is used between servers, storage platforms and accelerators in AI training, clustered databases, high-performance computing and distributed storage environments.

1

Lower processing overhead

By reducing conventional network processing, RDMA cuts latency and frees processors to run applications instead of moving data.

2

Faster data movement

Efficient memory-to-memory transfer helps distributed workloads share large datasets more quickly across multiple connected systems.

3

Fabric design matters

Suitable switching must provide bandwidth and control congestion, traffic priority and packet loss to maintain consistent RDMA performance.

The Role of RDMA in Modern IT Environments

RDMA supports workloads that move large volumes of data rapidly between servers, storage systems, and accelerators across the network.

The approach you choose determines how effectively CPUs and GPUs are reserved for application processing, how well clustered workloads perform, and whether latency, congestion, or incompatibility limits the value of your infrastructure.

As a partner to vendors including Cisco, HPE Aruba, Juniper, and Arista, we specify RDMA-ready networking against your requirements, so you're not overspending on bandwidth you won't use or left short on switching, adapter, and congestion-management support needed for technologies such as RoCE.

How RDMA Works

RDMA lets a network adapter move data directly between registered memory areas on compatible systems. This avoids extra processor work and software layers during each transfer.

For IT teams

This cuts latency and reduces CPU load. Teams can introduce RDMA between selected compute or storage systems first, then expand it across a cluster. It also helps validate compatibility and network settings across the full data path.

The diagram below shows how rdma works

Why Organisations Choose RDMA

RDMA helps organisations move data faster with lower latency and processor overhead across connected compute, storage and accelerator infrastructure.


Enabling Ultra-Low Latency Networking

RDMA moves data directly between system memory locations, bypassing conventional processing to help reduce communication delays.

Reducing CPU Utilisation

Less processor involvement in network transfers leaves more CPU capacity for applications, calculations and other workload-processing requirements.

Accelerating Data Transfers

Direct memory access moves large data volumes rapidly between servers, storage systems and accelerators without repeated operating-system intervention.

Improving Application Performance

Faster system communication reduces application wait times, improving responsiveness across supported databases, storage platforms and distributed services.

Supporting AI & HPC Workloads

RDMA supports rapid data exchange in clustered AI and high-performance computing environments, helping systems work together more effectively.

Increasing Infrastructure Efficiency

By reducing data-transfer delays and processor overhead, RDMA helps gain greater productive value from compute, storage and accelerator resources.

Common Enterprise Use Cases

RDMA supports high-performance enterprise workloads by reducing transfer latency, lowering CPU overhead, and improving data movement between interconnected systems.


Reducing Network Latency

Transfers data directly between connected systems with minimal CPU and operating-system involvement, reducing delays in latency-sensitive server communications.

Accelerating Distributed Storage

Helps storage clusters move data between hosts and nodes faster, improving access speeds, replication performance, and synchronisation efficiency.

Supporting High-Performance Computing

Enables compute nodes to exchange data rapidly during parallel processing, reducing wait times across simulations, modelling, and scientific calculations.

Improving GPU Communication

Works with compatible GPU memory-transfer technologies to improve data exchange between accelerated servers in distributed AI environments.

Increasing Application Performance

Reduces network-processing overhead for data-intensive applications, freeing CPU capacity for databases, analytics platforms, and other core workloads.

Enabling Real-Time Data Processing

Supports rapid system-to-system data exchange for transaction processing, streaming analytics, and other services where delays affect outcomes.

Key Considerations When Deploying RDMA

End-to-end validation avoids unsupported applications, unstable transports and hidden bottlenecks that undermine low-latency data movement at scale.


01

Network Compatibility

Confirm adapters, switches, operating systems, drivers and the selected RDMA transport interoperate so direct-memory transfers function across the complete path.

02

Latency Optimisation

Measure end-to-end latency, queueing and congestion under representative load so tuning decisions target the components actually delaying RDMA traffic.

03

CPU Offloading

Validate adapter offload capabilities and monitor processor utilisation so RDMA releases meaningful CPU capacity rather than shifting overhead elsewhere.

04

Storage Integration

Confirm storage targets, initiators, multipathing and failover mechanisms support the chosen RDMA transport so accelerated access remains resilient and supportable.

05

Application Support

Verify applications and middleware are designed and licensed for RDMA so deployment delivers usable acceleration without unsupported configuration changes.

06

Infrastructure Scalability

Model endpoints, queue pairs, routes and telemetry growth so hosts and fabrics retain stable performance as RDMA-enabled workloads expand.

Technology Comparison: RDMA vs Traditional TCP/IP Networking

The choice depends on whether application latency and CPU overhead justify specialised application, adapter and network requirements.

RDMA Traditional TCP/IP Networking
Data-transfer and CPU processing model RDMA transfers data directly between application memory on supported systems, reducing kernel involvement and processor copying. Traditional TCP/IP moves data through the operating-system network stack, using established buffering, congestion control and socket interfaces.
Best-fit low-latency workloads AI training, HPC, distributed storage and other supported workloads with frequent, latency-sensitive transfers between servers. General enterprise applications, web services, user traffic and mixed networks requiring broad compatibility and routability.
Latency, throughput and CPU overhead Reduces latency and CPU utilisation while increasing transfer efficiency, provided the application and complete network path support the selected RDMA transport. Provides dependable performance across diverse networks, although protocol processing and memory copies add overhead for the most demanding data transfers.
Application, adapter and network requirements Requires RDMA-aware applications or middleware, compatible NICs, drivers, firmware and a correctly configured RoCE, iWARP or InfiniBand fabric. Uses widely available operating-system tools, IP routing, security controls and troubleshooting skills without specialised application interfaces.
What it is not built for Applications without RDMA support or networks that cannot provide compatible adapters, drivers and transport behaviour end to end. Ultra-low-latency or CPU-sensitive cluster communication where conventional network-stack processing becomes a measurable workload bottleneck.

Enterprise Platforms We Recommend

Arista, HPE Aruba, Juniper, and Cisco platforms each suit different RDMA environments, operational models, and fabric requirements. Here's where each one fits best.


Arista Data Centre Switching product

Arista Data Centre Switching

Best for: Large AI or financial-services estates with experienced automation teams supporting low-latency memory-to-memory transfers across high-performance Ethernet fabrics.

Strengths
  • Supports high-speed RoCE fabrics for reduced host processing overhead
  • EOS controls congestion behaviour to protect latency-sensitive RDMA traffic
  • ECMP and MLAG preserve fabric paths during link failure events
  • CloudVision automates fabric change with fewer device-level checks
HPE Aruba Data Centre Switching product

HPE Aruba Data Centre Switching

Best for: Mid-sized private-cloud estates with lean teams extending familiar Aruba operations into the data centre for low-latency memory-to-memory Ethernet transport.

Strengths
  • Supports high-speed RoCE fabrics with reduced operating-system processing
  • AOS-CX controls congestion settings for latency-sensitive RDMA workloads
  • VSX and ECMP maintain alternative paths across resilient leaf-spine fabrics
  • Preserves operational familiarity across campus and smaller data centre estates
Juniper Data Centre Switching product

Juniper Data Centre Switching

Best for: Mid-sized and large data centres with automation-led teams prioritising intent-based fabric assurance for low-latency memory-to-memory Ethernet transfers.

Strengths
  • Supports high-speed RoCE fabrics with reduced host processing overhead
  • Junos controls buffering and congestion for latency-sensitive RDMA traffic
  • ECMP and EVPN multihoming preserve paths across resilient fabrics
  • Apstra identifies drift against intent before wider service impact
Cisco Data Centre Switching product

Cisco Data Centre Switching

Best for: Large Cisco-integrated data centres with dedicated fabric teams and formal policy control supporting low-latency memory-to-memory transfers across Ethernet fabrics.

Strengths
  • Supports high-speed RoCE fabrics with reduced operating-system processing
  • NX-OS or ACI protects latency-sensitive RDMA from burst traffic
  • ECMP and vPC maintain alternative paths across distributed fabrics
  • Nexus Dashboard centralises fabric policy, telemetry, and automation
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Related Technology Guides

RDMA connects closely with high-speed networking, accelerated compute and storage; these guides show where direct memory access delivers practical value.

RoCE (RDMA over Converged Ethernet)

Understand how RDMA is transported across suitably configured Ethernet networks for AI, compute and storage traffic.

Read the guide

High-Performance Computing (HPC)

Why clustered compute workloads use direct memory transfers to reduce communication delay between nodes.

Read the guide

NVLink

Explore how NVLink handles GPU communication within supported systems while RDMA moves data between networked hosts.

Read the guide

NVMe & NVMe-oF

Learn how NVMe-oF can use RDMA transports to extend low-latency flash access across a storage network.

Read the guide

Related Technology Platforms

Explore the infrastructure platforms that use direct memory access to reduce transfer overhead between compute, GPU and distributed storage systems.

AI Networking

Cut CPU involvement when transferring data between networked GPU, compute and storage systems.

View AI Networking Platforms

Data Centre Switching

Provide for high-throughput, low-latency traffic across fabric paths engineered for direct memory transfers.

View Data Centre Switching

AI and GPU Servers

Coordinate distributed AI and accelerated workloads across servers without conventional network-processing overhead.

View AI & GPU Servers

Storage Servers

Accelerate data access, replication and synchronisation between compute nodes and distributed storage systems.

View Storage Server Platforms
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FAQ

What is RDMA and how does it work?

RDMA transfers data directly between the application memory of compatible systems, reducing processor and operating-system overhead compared with conventional network communication methods.

Specialised adapters and software create protected memory regions that authorised applications can access directly. This shorter data path lowers delay, speeds repeated transfers and leaves more processor capacity available for AI, storage, simulation and other demanding workloads.

How does RDMA differ from traditional TCP/IP networking?

Traditional TCP/IP uses operating-system and processor resources to handle network traffic, while RDMA removes several processing steps to reduce latency and CPU utilisation.

TCP/IP remains the practical choice for most enterprise applications because it is widely supported, routable and familiar to run. RDMA becomes more relevant where network delay directly limits distributed compute, accelerated processing or storage performance enough to justify added compatibility and management requirements.

Which network technologies can carry RDMA traffic?

RDMA traffic can run across InfiniBand, Ethernet-based RoCE and TCP-based iWARP, with each option requiring different infrastructure, compatibility and operational expertise.

InfiniBand provides a dedicated high-performance fabric, RoCE brings RDMA to engineered Ethernet, and iWARP uses TCP networking. The right transport should match application support, current architecture, scale and team capability, so performance gains do not introduce unnecessary complexity.

Which workloads benefit most from RDMA?

RDMA benefits AI, HPC, distributed storage and real-time processing workloads where frequent data transfers or network delays directly limit application performance.

GPU clusters, scientific simulations and compatible storage or database platforms are common candidates. Standard business applications usually gain little because their performance is rarely constrained by memory-copying overhead or very small network delays, making workload evidence important before investment.

What infrastructure is required to deploy RDMA?

RDMA requires compatible applications, adapters, drivers, operating systems and network infrastructure working correctly together across the complete communication path.

The chosen transport also affects routing, congestion management, firmware and monitoring requirements. Validating these dependencies before deployment helps prevent a single incompatibility or network misconfiguration from undermining expected performance improvements and creating avoidable operational risk.

When might RDMA be unnecessary for an organisation?

RDMA may be unnecessary when current networking already meets application needs or when storage, compute or software constraints are the real issue.

Adding RDMA introduces specialist compatibility, configuration and troubleshooting requirements that ordinary workloads may not justify. Identifying where delays actually originate helps avoid investing in faster networking when the underlying performance constraint sits elsewhere in the stack.

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