Why NVLink and NVSwitch Are Essential for Training Massive AI Models

In the era of large foundation models, training requires massive GPU resources and long training times, making multi‑GPU communication a critical challenge.

Distributed Communication and NVLink

Distributed communication connects multiple nodes in a computer system so they can exchange data and cooperate on a common task. NVLink is a high‑speed, low‑latency communication technology typically used to link GPUs to each other or to other devices, enabling high‑performance computing and data transfer.

Characteristics of Foundation Models

Data scale : Foundation models often use self‑supervised learning, reducing labeling costs while leveraging massive datasets to improve generalization and performance.

Parameter scale : As model parameters grow, the models can capture more complex relationships, pushing accuracy beyond traditional architectures.

Compute demand : The combination of huge data and parameter counts exceeds the capacity of a single machine, requiring both advanced hardware and AI frameworks that support distributed parallel training.

Therefore, distributed parallel strategies are required to handle these challenges.

Data Parallel

Data Parallel (DP) replicates the entire model on each GPU, each processing a distinct subset of the data. Every GPU performs forward and backward passes independently on its data slice.

Model Parallel

Model Parallel (MP) distributes different parts of a large neural network across multiple compute nodes, allowing training of models that cannot fit on a single device. MP can be further divided into Pipeline Parallel (PP) and Tensor Parallel (TP).

AI Framework Distributed

All parallel strategies essentially split the model either vertically or horizontally and place the pieces on different machines, fully utilizing available compute resources.

NVLink and NVSwitch

NVLink is an advanced bus and communication protocol that uses a point‑to‑point, serial architecture to connect CPUs to GPUs or GPUs to GPUs.

NVSwitch is a high‑speed interconnect chip that provides up to 18 NVLink connections, enabling rapid data transfer among multiple GPUs.

These technologies increase bandwidth and lower latency for GPU clusters, boosting overall system performance.

NVLink Evolution

From the Pascal architecture through Hopper to the 2024 Blackwell architecture, NVLink has progressed through five generations. Bandwidth grew to 1800 GB/s, and the number of GPUs that can be interconnected rose from 4 in the first generation to 18 in later generations, though the maximum remains 18 in Blackwell.

Summary and Reflections

Distributed communication technologies such as NVLink and NVSwitch are vital for efficient multi‑GPU training in the AI large‑model era.

Diverse parallel strategies—including data parallel, model parallel, pipeline parallel, and tensor parallel—significantly improve training efficiency and resource utilization.

The continual evolution of NVLink and NVSwitch provides higher inter‑GPU bandwidth and lower latency, empowering large‑scale AI workloads.