What is the Difference Between NVLink and InfiniBand?

Vishnu Subramanian

Founder @JarvisLabs.ai

NVLink is designed for ultra-high-speed GPU-to-GPU communication within a single server, while InfiniBand connects multiple servers across clusters and data centers. NVLink offers higher bandwidth for GPU workloads (up to 1.8TB/s), while InfiniBand excels at scalable, low-latency networking between nodes.

Understanding the Fundamentals

NVLink and InfiniBand serve fundamentally different roles in high-performance computing infrastructure. While both technologies aim to accelerate data transfer, they operate at different scales and serve distinct purposes in modern data centers.

NVLink is NVIDIA's proprietary high-speed interconnect technology specifically designed for GPU-to-GPU and GPU-to-CPU communication within the same server or node. It creates direct, high-bandwidth connections between processors without going through traditional PCIe buses.

InfiniBand is an industry-standard networking protocol that connects multiple servers, storage systems, and other devices across clusters and data centers. It's designed for server-to-server communication and building large-scale computational networks.

Technical Specifications Comparison

Bandwidth and Performance

NVLink Performance:

• Fifth-generation NVLink vastly improves scalability for larger multi-GPU systems by enabling GPUs to share memory and computations for training, inference, and reasoning workflows. A single NVIDIA Blackwell GPU supports up to 18 NVLink 100 gigabyte-per-second (GB/s) connections for a total bandwidth of 1.8 terabytes per second (TB/s)
• More than 14x the bandwidth of PCIe Gen5
• Direct memory sharing between GPUs eliminates traditional memory copying overhead

InfiniBand Performance:

• Current InfiniBand speeds range from 100Gb/s EDR to 200Gb/s HDR, with the latest 400Gb/s NDR now shipping
• InfiniBand achieves significantly lower latency compared to Ethernet. InfiniBand switches streamline layer 2 processing and employ cut-through technology, reducing forwarding latency to below 100ns
• Supports RDMA (Remote Direct Memory Access) for CPU-free data transfers

Architecture and Design Philosophy

NVLink: Maximizing GPU Performance

NVLink addresses the traditional bottleneck of PCIe connections in GPU-intensive workloads. NVLink enables high-speed direct interconnection between GPUs within the server, allowing:

• Unified memory space across multiple GPUs
• Direct GPU-to-GPU memory access without CPU involvement
• Coherent memory operations between processors
• Optimized for parallel computing and AI workloads

InfiniBand: Scalable Cluster Networking

InfiniBand (IB) is a communication network that allows data to flow between CPUs and I/O devices, with up to 64,000 addressable devices. It uses a point-to-point connection in which each node communicates directly with other nodes over dedicated channels, providing:

• Switched fabric architecture for massive scalability
• Hardware-based transport protocol offloading
• Advanced congestion control and quality of service
• Support for various network topologies (fat tree, mesh, torus)

Use Cases and Applications

When to Choose NVLink

NVLink is ideal for scenarios requiring maximum GPU performance:

• Large Language Model Training: Training models like GPT or LLaMA that require massive GPU memory and compute
• Deep Learning Research: Multi-GPU workloads where GPUs need to share data frequently
• Real-time AI Inference: Applications demanding ultra-low latency GPU communication
• Scientific Computing: Simulations requiring tightly coupled GPU processing

At JarvisLabs, our H100 and H200 instances leverage NVLink for optimal performance. Our H100 bare-metal configurations with 8 GPUs provide 640GB of combined VRAM and massive parallel processing power for the most demanding AI workloads.

When to Choose InfiniBand

InfiniBand excels in large-scale, distributed computing environments:

• Supercomputing Clusters: InfiniBand dominated the global Top 500 supercomputer list, holding an impressive 51.8% share
• High-Performance Storage: Connecting storage arrays to compute clusters
• Database Clusters: Distributed databases requiring low-latency node communication
• Scientific Research: Large-scale simulations across multiple servers

Cost Considerations

NVLink Costs:

• NVLink usually involves a higher investment due to its tie with NVIDIA GPUs
• Requires NVIDIA hardware ecosystem
• Higher costs offset by dramatically improved GPU utilization

InfiniBand Costs:

• InfiniBand, being a well-established market player, offers more pricing options and configuration flexibility
• Multiple vendor options (primarily NVIDIA/Mellanox)
• Lower per-port costs for large-scale deployments

Hybrid Architectures: Best of Both Worlds

Large-scale data centers and supercomputing systems often opt for a hybrid interconnect architecture that embraces both NVLINK and InfiniBand technologies. NVLINK is frequently employed to interconnect GPU nodes, enhancing the performance of compute-intensive and deep learning tasks. Meanwhile, InfiniBand takes charge of connecting general-purpose server nodes, storage devices, and other critical equipment within the data center.

This hybrid approach allows organizations to:

• Maximize GPU performance within nodes using NVLink
• Scale across multiple nodes using InfiniBand
• Optimize both intra-node and inter-node communication

Future Roadmap

NVLink Evolution:

• NVLink 5.0 already supports 576 fully connected GPUs
• Focus on deeper integration within NVIDIA ecosystem
• Emphasis on AI and accelerated computing workloads

InfiniBand Advancement:

• Current roadmap shows a projected demand for higher bandwidth with GDR 1.6Tb/s InfiniBand products planned for 2028 timeframe
• Continued emphasis on open standards and vendor compatibility
• Enhanced in-network computing capabilities

Making the Right Choice

The decision between NVLink and InfiniBand isn't typically either/or—they serve different architectural needs:

• Choose NVLink when you need maximum GPU-to-GPU performance within a single system
• Choose InfiniBand when you need to scale across multiple servers and build large clusters
• Consider both in a hybrid architecture for comprehensive high-performance computing solutions

For most AI researchers and ML engineers starting their journey, focusing on NVLink-enabled systems like our H100 instances will provide immediate performance benefits. As your computational needs scale beyond single-node capabilities, InfiniBand becomes essential for building larger, distributed systems.

Understanding these technologies helps you architect solutions that match your performance requirements and budget constraints, whether you're training the next breakthrough AI model or running complex scientific simulations.