What is remote direct memory access (RDMA)?

Remote direct memory access (RDMA) defined

Remote direct memory access (RDMA) is a networking technique that lets one computer read or write memory on another computer. It does this across a network, without involving the operating system or processor on either side.

Read on to learn more about:

• What RDMA is and the problem it solves

• How direct memory transfers work in the system

• Key differences between RDMA and standard TCP/IP networking

• Primary transport options across modern data centers

• Practical use cases across artificial intelligence and distributed storage

• Implementation requirements for enterprise networks

What problem does RDMA solve?

Standard networks put a heavy load on main processors. When two servers interact, the operating system (OS) acts as a middleman. The sending server copies data from applications into temporary OS memory.

It packages the data into packets and sends it over the network. The receiving server repeats these steps in reverse. It stops what it is doing and copies the data into the target app.

This multi-step path works well for general business applications. However, modern workloads like large language models (LLMs) and distributed databases move massive volumes of data every second.

When thousands of chips train an AI model, they must share information constantly. If data gets stuck in OS queues or takes too long to copy, your fast chips sit idle waiting for updates. This delay slows down your entire system.

RDMA solves this problem. It moves data straight from one server's memory to another across the network, skipping the OS entirely.

How RDMA works

RDMA replaces software steps with fast hardware. It uses special network cards to manage memory directly.

This direct transfer model functions through three core mechanics:

1. Kernel bypass

Applications talk directly to the network interface card from user space. The OS kernel doesn’t process packet headers, run protocol stacks, or handle software interrupts during the data transfer.

2. Zero-copy transfers

Data moves directly from the sending memory address to the network card. It then travels across the network, and it lands directly in the destination memory address without intermediate buffer copies in system RAM.

3. Memory registration

Apps tell the network card which memory spaces to use before sending data. The card locks these spots in hardware so transfers stay secure and fast.

In AI infrastructure, this capability extends directly to accelerators through GPUDirect RDMA. Network cards move model data directly between GPU memories across the network. It never touches regular system RAM.

RDMA vs. traditional TCP/IP and transport protocols

To understand when to use RDMA, you can start by comparing its data path against conventional network stacks.

Standard TCP/IP is built for broad network compatibility. But routing packets through the OS creates delays and consumes CPU power. RDMA offloads this work to hardware and writes straight to destination memory.

TCP/IP is very good for web services, but RDMA is needed when network delay or CPU load slows down your app.

Primary RDMA transport protocols

RDMA capabilities can run across different physical networks and link layers. Three main transport implementations exist in modern data centers today:

1. RDMA over Converged Ethernet (RoCE)

RoCE brings direct memory transfers to regular Ethernet networks. The earliest version, RoCEv1, only worked between servers on the same local network segment. Today, most teams use RoCEv2. It packages RDMA data so it can route across different subnets using standard enterprise network switches.

2. InfiniBand

InfiniBand is a custom network built for supercomputing. It offers native RDMA support, hardware-managed flow control, and very low latency. It uses custom cables and switches, so it runs as a separate network from your main Ethernet setup.

3. iWARP

iWARP implements RDMA directly on top of standard TCP/IP. Because it uses TCP, it works on regular Ethernet switches without special setup. While it is simpler to deploy than RoCEv2, it carries slightly higher latency due to the underlying TCP protocol overhead.

Choosing the right network setup helps teams get better speed and efficiency out of their most demanding apps.

Key benefits of RDMA

Understanding how RDMA moves data helps explain why it’s become the standard for high-speed clusters. Moving data directly in hardware gives teams several clear advantages:

• Ultra-low latency: Cutting out OS queues drops network delay to low single-digit microseconds.

• Near-zero CPU load: Because the network card moves the data, your CPUs stay focused on running your actual apps.

• Maximum network throughput: Direct memory access efficiently fills high-capacity network links, allowing clusters to sustain 400G and 800G line rates without saturating host system buses.

• Predictable tail latency: Moving transfer logic into hardware eliminates software scheduling delays, delivering consistent communication speeds across large clusters.

These performance gains make RDMA essential for workloads that cannot afford data transfer delays.

Common use cases for RDMA

Modern apps create heavy traffic between servers. Because of this, RDMA serves as the backbone for several data-heavy systems:

• Distributed AI training and inference: AI models share weights and data across thousands of chips. RDMA keeps compute chips busy by removing network delays.

• High-speed distributed storage: NVMe over Fabrics (NVMe-oF) uses RDMA to access remote flash storage pools at speeds comparable to locally attached NVMe drives.

• Clustered databases and analytics: Distributed databases and caching tiers rely on RDMA for high-speed replication, state synchronization, and low-latency transaction processing.

• High-performance computing (HPC): Scientific modeling, financial simulations, and weather forecasting workloads use RDMA to pass messages rapidly across massive compute clusters.

While the benefits for these workloads are clear, capturing them requires careful network planning and operational oversight.

RDMA implementation considerations

Deploying RDMA in enterprise data centers involves more than simply installing new network cards. Teams must design and monitor the underlying network fabric with specific requirements in mind:

Lossless Ethernet configuration

RoCEv2 depends on a lossless network environment to perform reliably. Network teams must configure priority-based flow control (PFC) to prevent dropped packets, alongside explicit congestion notification (ECN) to manage network congestion before buffers overflow.

Monitoring beyond standard metrics

Standard network dashboards might show green lights and good speeds even when apps are slowing down. To catch hidden slowdowns, you need to monitor if switches are pausing traffic, check how full switch memory buffers are, and track actual data transfer speeds from start to finish.

Phased adoption and validation

Teams moving to RDMA should validate fabric configuration in a test environment first, verifying that switches, network cards, and application frameworks handle congestion and failover gracefully before running production traffic.

With a well-tuned network in place, teams can support their most demanding apps.

Conclusion

As distributed AI and fast flash storage grows, network speed directly shapes app performance. RDMA removes traditional OS delays by turning the network into an extension of local memory.

By pairing direct memory transfers with a well-engineered, lossless network foundation, organizations can maximize their compute investments, reduce latency, and build infrastructure ready for next-generation workloads.