InfiniBand vs Ethernet: Key Differences in AI Networking

Published on February 12, 2026

Introduction : Artificial intelligence (AI) workloads such as large language model (LLM) training, distributed deep learning and high performance computing (HPC) demand ultra-fast, low latency communication between thousands of GPUs and compute nodes. The choice of networking technology plays a critical role in determining AI system performance, scalability and cost efficiency. Among the leading interconnect technologies, InfiniBand and Ethernet have emerged as the two dominant contenders for AI data center networking. This page explains both and later explore major difference between InfiniBand and Ethernet which are essential for designing next generation AI data centers and distributed computing platforms.

InfiniBand

It has long been the gold standard for high performance computing, offering ultra low latency, high bandwidth and native Remote Direct Memory Access (RDMA) capabilities optimized for GPU clusters.

InfiniBand was designed from the ground up for High Performance Computing (HPC). Unlike general purpose networking, InfiniBand focuses on moving data between processors and memory with the absolute minimum amount of delay (latency).

How it works: It uses a “Credit Based” flow control system. A sender only transmits data when the receiver confirms it has the buffer space to hold it. This makes InfiniBand natively lossless and packets are almost never dropped.

Benefit: It significantly reduces the burden on the CPU by using RDMA (Remote Direct Memory Access), allowing data to move from one server’s memory to another without involving the operating system.

Ethernet

Ethernet is the most widely used networking standard in the world. Historically, it was a “best effort” network, meaning if the network got too busy, it would simply drop packets and ask for them to be resent later. While fine for the internet, this was a problem for AI.

Ethernet is “reclaiming” the data center through innovations like RoCE v2 (RDMA over Converged Ethernet) and new standards from the Ultra Ethernet Consortium.

How it works: New features like Priority Flow Control (PFC) and Explicit Congestion Notification (ECN) allow Ethernet to mimic the lossless behavior of InfiniBand.

Benefit: It is based on open standards, making it easier to scale across massive data centers with equipment from many different vendors.

Comparison between InfiniBand and Ethernet (2026 Landscape)

Summary

InfiniBand and Ethernet represent two fundamentally different approaches to AI networking. InfiniBand excels in delivering deterministic low latency, high throughput, and lossless communication, making it ideal for tightly coupled AI training clusters and HPC environments. However, it typically comes with higher hardware costs, vendor lock-in, and specialized operational requirements.

Ethernet, on the other hand, offers an open ecosystem, broad vendor support and lower total cost of ownership, making it the dominant choice for cloud data centers and hyperscale AI deployments. With advancements such as RoCE, congestion control mechanisms, and next-generation Ultra Ethernet standards, Ethernet is rapidly closing the performance gap with InfiniBand while maintaining scalability and operational simplicity.

Ultimately, the choice between InfiniBand and Ethernet depends on workload requirements, budget, scalability goals and ecosystem strategy. As AI clusters continue to scale to hundreds of thousands of GPUs, both technologies will coexist; InfiniBand leading in performance critical training fabrics and Ethernet driving large scale, cost efficient AI infrastructure.