What is the Difference Between NVLink and InfiniBand?

NVLink and InfiniBand are both built to move data fast, but they solve problems at different scales. NVLink is NVIDIA's proprietary interconnect for wiring GPUs together inside a single server. InfiniBand is an industry-standard networking fabric for wiring whole servers, storage arrays, and other nodes together across a cluster. Confusing the two is easy because both show up on the spec sheet of any serious AI cluster, but they're rarely substitutes for each other.

Understanding the Fundamentals

NVLink creates direct, high-bandwidth links between GPUs (and, on some platforms, between GPUs and CPUs) within the same server, bypassing the general-purpose PCIe bus entirely. InfiniBand, by contrast, is a networking protocol, standardized by the InfiniBand Trade Association, that connects independent machines: compute nodes, storage nodes, anything with a network adapter, over cabling that can span a data center. One scales a single machine's GPUs; the other scales the number of machines.

Technical Specifications Comparison

Bandwidth and Performance

NVIDIA's fifth-generation NVLink, shipping on Blackwell GPUs like the B200, gives each GPU up to 18 links at 100 GB/s apiece, for 1.8 TB/s of total bandwidth, over 14 times what a PCIe Gen 5 slot delivers. Because NVLink lets GPUs address each other's memory directly, multi-GPU workloads avoid the copy overhead that ordinary networked memory transfers carry.

InfiniBand's current generation, NDR, runs at 400 Gb/s per port, following EDR (100 Gb/s) and HDR (200 Gb/s) in the standard's naming progression, with 800 Gb/s XDR gear now reaching the market. InfiniBand's defining feature is RDMA (Remote Direct Memory Access), which lets one node read or write another node's memory without involving either machine's CPU, which is what keeps its latency low even at cluster scale.

Architecture and Design Philosophy

NVLink

NVLink exists to remove the PCIe bottleneck from GPU-to-GPU communication inside a server. It gives a group of GPUs a shared, coherent memory space, so a GPU can read or write another GPU's memory directly instead of staging the data through the host. That's what makes tightly-coupled parallel workloads, like sharding a single large model's layers or attention heads across GPUs, practical without the interconnect becoming the bottleneck.

InfiniBand

InfiniBand is built to scale outward instead of inward. It uses a switched fabric where every node connects point-to-point through dedicated channels rather than sharing a bus, which is what lets a single fabric address tens of thousands of devices. Beyond raw bandwidth, InfiniBand pushes transport-protocol handling into the network adapter itself, and layers on congestion control and quality-of-service features aimed at keeping large, busy fabrics stable, across topologies like fat-tree, mesh, and torus.

Use Cases and Applications

When to Choose NVLink

NVLink is the right layer when the bottleneck is GPU-to-GPU traffic inside one box: training large language models whose layers span multiple GPUs, any multi-GPU workload where the GPUs exchange activations or gradients on every step, inference for models too large to fit on a single GPU's memory, and latency-sensitive real-time inference. An 8-GPU NVLink-connected server built on the H100 or H200, for instance, can present hundreds of gigabytes of pooled GPU memory to a single job.

When to Choose InfiniBand

InfiniBand is the right layer once the job spans more machines than fit in one chassis: distributed training across dozens or hundreds of nodes, supercomputing clusters (InfiniBand has historically powered a majority of the fastest systems on the Top500 list), high-performance shared storage, and distributed databases that need low node-to-node latency.

Cost Considerations

NVLink's cost is bundled into the GPU platform: it only comes with NVIDIA's SXM-form-factor, high-end datacenter parts and the server boards built to host them, so you pay for it as part of the hardware tier you choose. InfiniBand is a separate, mature market of its own, with switches and adapters from several vendors (predominantly NVIDIA/Mellanox today), which gives buyers more configuration flexibility and generally lower cost per port as a deployment scales up.

Hybrid Architectures

Every large GPU cluster runs both. NVLink handles the GPUs within each server, giving that server's GPUs a fast shared-memory domain, while InfiniBand connects the servers themselves, along with shared storage, into one fabric. This two-tier design is exactly what lets frameworks split a training job into a fast intra-node dimension (NVLink) and a slower but far larger-scale inter-node dimension (InfiniBand), which is the basic shape behind data-parallel and pipeline-parallel training across hundreds of GPUs.

Aquanode's marketplace lists multi-GPU H100, H200, and B200 instances with NVLink already wired up inside each node, so a training job gets the intra-node bandwidth without you having to assemble an HGX platform yourself; see pods for available multi-node configurations.

Future Roadmap

NVLink 5.0 already supports domains of up to 576 fully-connected GPUs through NVLink Switch, and NVIDIA's roadmap points toward deeper integration across its own GPU and networking stack. InfiniBand's roadmap follows the same doubling cadence it always has, with the InfiniBand Trade Association's planned GDR generation targeting 1.6 Tb/s per port later this decade, alongside continued investment in open standards and in-network computing features.

Making the Right Choice

The two technologies aren't competitors, they're layers. Choose NVLink when the problem is GPU-to-GPU bandwidth within one server, choose InfiniBand when the problem is connecting many servers into one cluster, and expect any cluster large enough to matter to use both at once, NVLink inside the box and InfiniBand between boxes. Understanding which layer your workload is actually bottlenecked on, covered in more depth alongside PCIe and general GPU architecture, is most of what it takes to size a cluster correctly instead of overspending on the wrong interconnect.