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How InfiniBand vs. RoCE compare for AI networking
InfiniBand and RoCE both offer distinct advantages to companies determining the best way to support their AI training strategies. But the cheaper option might not be the best one.
AI training is divided across many accelerators that constantly exchange gradients, activations and other data. These exchanges must stay synchronized. If congestion or delays slow down one network path, they can slow other GPUs, increasing training time and leaving compute resources idle.
This makes the choice of a scale-out network fabric an important part of AI infrastructure design. Both InfiniBand and Remote Direct Memory Access over Converged Ethernet (RoCE) move data between systems with minimal CPU involvement. But each differs in how it manages congestion, routes traffic and maintains performance.
For enterprises, which option to choose -- InfiniBand vs. RoCE -- hinges on deployment and management requirements, hardware and vendor choices, scalability and the engineering effort needed to keep the fabric running. The choice comes down to balancing performance consistency, operational complexity, vendor flexibility and total cost of ownership (TCO).
How InfiniBand and RoCE sustain AI performance
Both fabrics provide high bandwidth and low latency, but each handles heavy traffic a bit differently. InfiniBand handles more congestion management within the fabric, while RoCE performance depends more on how the Ethernet network is configured.
InfiniBand
InfiniBand manages network traffic with flow control and congestion management mechanisms built into the fabric. Its credit-based flow control requires a sending port to have available downstream buffer space before transmitting, which helps prevent buffer overflow drops on the link.
The fabric does not treat all network traffic as a single stream. Virtual lanes create separate logical channels over the same physical link, preventing one traffic class from blocking another. If congestion still builds, the fabric can signal endpoints to slow their transmission rates.
InfiniBand switches can also use dynamic routing to send traffic over a less congested path when multiple paths are available. These mechanisms help the fabric maintain consistent latency and throughput.
RoCE
RoCEv2 takes a different approach because it carries Remote Direct Memory Access (RDMA) traffic over routable UDP/IP Ethernet. RoCE fabrics have traditionally relied on priority flow control to pause higher-priority traffic before switch buffers overflow. But frequent pauses can also disrupt performance because other traffic sharing that priority can be forced to wait.
RoCE also uses explicit congestion notification, which enables switches to mark IP packets when queues begin to build. Congestion control mechanisms, such as data center quantized congestion notification (DCQCN), use those marks to reduce the sender's transmission rate.
Performance ultimately depends on network optimization, as large RoCE deployments indicate. Meta, for example, found that the default DCQCN configuration degraded performance on its 400 Gigabit Ethernet (GbE) endpoints.
To avoid the decline in performance, Meta improved traffic distribution across the network by using multiple RDMA queue pairs and enhancing equal-cost multi-path routing. This increased AllReduce performance by up to 40% compared with its baseline equal-cost multipath (ECMP) setup. These adjustments let Meta operate RoCE at a very large scale, according to the company.
In a 2024 trial, Meta built two versions of a 24,576-GPU H100 cluster, one with RoCE and the other with InfiniBand. Each ran large generative AI workloads without network bottlenecks, Meta said. This shows the practical difference lies in how much network engineering is needed to maintain performance as the cluster grows.
How to deploy and operate each fabric
The operational tradeoff between InfiniBand vs. RoCE is more complex than the choice between specialized networking and familiar Ethernet.
InfiniBand uses dedicated switches and host channel adapters, with a subnet manager controlling the fabric. The manager finds the network topology, assigns local identifiers and programs forwarding paths. Managing this environment requires InfiniBand-specific knowledge and tools.
RoCE builds on Ethernet and IP technologies that enterprise network teams already use. It also gives organizations a broader range of hardware options. Enterprises can choose switches and NICs from multiple vendors -- such as Arista, Cisco and Juniper -- which offer data center switches designed to support RoCE traffic. The current Ethernet products extend to 800 GbE and higher speeds.
This approach provides companies with more flexibility in purchasing and network design. Teams continue to use familiar technologies such as routed leaf-spine networks, Border Gateway Protocol and ECMP routing. Companies can also use many of their existing Ethernet management and monitoring tools.
But RoCE is not a conventional Ethernet mechanism with added RDMA capability. The network still has to be designed for the synchronized, high-bandwidth traffic generated by AI workloads.
InfiniBand vs. RoCE TCO
Regardless of the model selected, the TCO for both include the following:
- NICs or host channel adapters.
- Optics.
- Cabling.
- Management software.
- Support.
- Power.
- Cooling.
- Future expansion.
Enterprises must also account for the costs of designing, testing and operating the fabric.
RoCE benefits from the larger Ethernet market, so enterprises can choose from more switch, NIC and optics suppliers. Competition between vendors also gives companies more purchasing flexibility. InfiniBand has a more concentrated hardware ecosystem, but its switches, adapters and management tools are integrated tighter.
Operational costs depend on the organization's existing skillset. InfiniBand has its own management model; enterprises might need to train their network teams accordingly. However, for organizations that already run high-performance computing environments, much of that expertise could already be in place.
RoCE can build on existing Ethernet and IP expertise, but it still requires specialized work. Teams need to configure and troubleshoot an RDMA fabric. They might also need to validate the topology, quality-of-service settings, congestion control parameters and telemetry as the cluster grows.
The economics also depend on how well the network keeps GPUs busy. A more expensive fabric can still be the better investment if it reduces communication delays and enables training jobs to finish quicker. In that case, the enterprise gets more productive GPU hours from the same accelerator investment.
The purchasing decision between InfiniBand and RoCE should focus on the total cost of delivering high-quality AI workload performance over the cluster's lifetime. The network with the lower initial price is not always the one with the lower total cost.
The choice between InfiniBand vs. RoCE
InfiniBand and RoCE are two network transport technologies that are appropriate for different scenarios.
InfiniBand is ideal for dedicated training clusters where predictable performance and scaling efficiency are important. Its integrated congestion control, routing and management can also reduce some of the engineering work required to operate the fabric.
RoCE, on the other hand, can be a better fit for enterprises that want to stay with Ethernet and use existing IP networking skills. RoCE works well in environments that combine training, fine-tuning, inference, storage and conventional IP traffic.
Abhishek Jadhav is a technology journalist covering AI infrastructure, semiconductors and advanced computing systems.
