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Kubernetes can support multi-node GPU training without assigning each pod an SR-IOV virtual function (VF). NVIDIA documents two main alternatives: an RDMA shared-device network paired with MacVLAN or IP over InfiniBand (IPoIB), and host-device networking. Shared-device profiles allow RDMA resources to be shared; host-device networking provides exclusive direct device access. Neither is a drop-in equivalent to per-pod VF allocation: the right fit depends on the fabric, tenancy and isolation requirements, device support, and whether the GPU data path needs GPUDirect RDMA.
How the alternatives differ from SR-IOV
These profiles change how Kubernetes exposes network hardware to workloads. A secondary network attachment alone does not establish RDMA or GPU-direct operation; those depend on compatible hardware and a coordinated software configuration.
| Profile | Fabric or network type | Resource and isolation model | Best fit to evaluate |
|---|---|---|---|
| RDMA shared device with MacVLAN | Ethernet/RoCE | RDMA resources are shared. NVIDIA describes this mode as suitable when RDMA device isolation between network namespaces is not required; it is not per-pod VF isolation. | Workloads whose tenancy model permits sharing and whose network segmentation needs suit MacVLAN. |
| RDMA shared device with IPoIB | InfiniBand | RDMA resources are shared rather than allocated as a dedicated VF to each pod. | InfiniBand clusters where the deployed operator release and device configuration support the profile. |
| Host-device RDMA | Depends on the supported device and network configuration | The quick-start profile describes direct device access and exclusive hardware access. Exclusive assignment limits concurrent use of that device by pods. | Software that needs direct control of a network device and can accept exclusive assignment. |
| SR-IOV RDMA (comparison) | Supported Ethernet/RoCE or InfiniBand setup, as configured | A NIC is divided into VFs; the relevant device plugin and CNI components provision VFs to pods, enabling per-pod VF allocation. | Clusters requiring dedicated VF allocation and its associated per-pod resource boundary. |
The table describes allocation models, not a guarantee of identical access control, scheduling behavior, or training performance. In particular, shared RDMA mode should not be described as providing the isolation of a dedicated VF.
Choose by tenancy, fabric, and GPU data path
Start with the tenancy boundary
Decide whether each training pod needs a dedicated network resource or whether its RDMA resources may be shared. If the isolation requirement is specifically a per-pod VF, the shared-device profiles do not meet that requirement on the evidence described here; retain SR-IOV as the candidate to validate. If sharing is acceptable, compare the shared profiles with the workload’s network segmentation and operational needs. If software needs exclusive direct access, assess host-device networking and account for the resulting limit on concurrent device assignments.
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Match the profile to the fabric
MacVLAN with shared RDMA is documented for RoCE; IPoIB with shared RDMA is the InfiniBand option. Confirm that the chosen profile, NIC, and network configuration are supported together on the target cluster. Do not assume a profile documented for one fabric transfers unchanged to the other.
Separate RDMA from GPUDirect RDMA
NVIDIA describes RDMA as memory-to-memory transfer that bypasses the CPU and kernel networking stack, and documents support for InfiniBand and RoCE. GPUDirect RDMA is a further requirement: it depends on compatible systems and coordinated Network Operator and GPU Operator configuration. Choosing MacVLAN, IPoIB, or host-device networking does not by itself guarantee GPU-direct transfers.
Check what Kubernetes allocates
Before choosing a profile, determine what resource the cluster advertises and schedules: a shared RDMA device, an exclusively assigned host device, or a VF. The distinction affects how pods can consume the network hardware and how many workloads can use a device concurrently. NVIDIA documents separate SR-IOV and RDMA shared-device plugins; verify the resource names and allocation behavior for the actual deployment rather than inferring them from the network attachment type.
Validate the profile against the target cluster
- Pin the software release. Select the intended NVIDIA Network Operator release, then use documentation for that release. NVIDIA documentation in the material available spans v25.10 quick-start examples, v26.4 overview material, and v26.12 platform-support listings; these are version-specific references, not one universal compatibility statement. Do not copy a v25.10 example as current installation guidance without checking the target release.
- Check the complete hardware and platform combination. Consult the official support matrix for the exact operating system, GPU, NIC, fabric, and relevant firmware and driver combination. A compatible high-speed NIC is a hardware prerequisite, but the exact adapter, server slot, firmware, port type, optics, and cabling must be checked for the system being deployed.
- Confirm network-profile compatibility. Verify support for the selected MacVLAN/RoCE, IPoIB/InfiniBand, host-device, or SR-IOV profile. NVIDIA warns that some network types cannot be combined on the same NIC; a cluster mixing profiles may need separate NICs.
- Verify the scheduled resource and access boundary. Check which device-plugin resource Kubernetes exposes, how the workload requests it, whether assignment is shared or exclusive, and whether the resulting access model meets the cluster’s tenancy requirements.
- Validate RDMA and GPU-direct operation separately. Test RDMA for the intended interface and confirm GPUDirect RDMA independently if the training job requires it. Check compatibility and configuration across the NIC, GPU, drivers, Network Operator, and GPU Operator rather than treating a successful secondary-network attachment as proof of either data path.
- Benchmark the actual training job. Use the intended multi-node topology, node count, GPU count, collective operations, and concurrency. Compare end-to-end training behavior and relevant network measurements under the same conditions for each viable profile. The cited NVIDIA materials do not establish a controlled head-to-head training benchmark or a universal performance winner.
What the available performance evidence can and cannot establish
NVIDIA’s quick-start guide presents distinct use-case profiles for SR-IOV RDMA, host-device RDMA, shared RDMA with IPoIB, and shared RDMA with MacVLAN. Any bandwidth or latency figures in such profile tables describe the documented use case; they are not controlled comparisons among those alternatives. They should not be used to claim that one profile will train faster across different hardware, topologies, or workloads.
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For a decision that affects training throughput, compare candidate profiles on the same cluster and workload. Hold topology and workload configuration constant, and record the results alongside the allocation model and software versions. This makes a measured result useful for that deployment without turning it into a general claim about all Kubernetes GPU clusters.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Practical selection rule
- Start by evaluating shared RDMA with MacVLAN for a supported RoCE setup, or shared RDMA with IPoIB for a supported InfiniBand setup, when sharing fits the tenancy model.
- Evaluate host-device networking when direct, exclusive device access is needed and the concurrency constraint is acceptable.
- Keep SR-IOV in consideration when dedicated per-pod VF allocation is a requirement.
- In every case, verify release-specific compatibility and benchmark the actual multi-node training workload before treating the profile as suitable.
These are candidate architecture profiles, not performance-equivalent substitutes. Make the choice from the required isolation boundary, fabric, device support, GPU data path, and measured behavior of the intended job.
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