NVLink vs PCIe: What’s the Difference for AI Workloads?

If you’re working on training large-scale models or deploying high-throughput inference systems, the type of GPU interconnect (PCIe or NVLink) you choose can make or break your performance. Both are powerful but they serve different needs.

So, how do you decide between the two? Let’s break it down for you.

Why You Need Interconnects in AI Workloads

In AI workloads, such as training large language models or running high-throughput inference, the speed of data movement between GPUs is crucial. 

• In model parallelism, GPUs need to share weights and gradients rapidly.
• In data parallelism, syncing parameters between GPUs is crucial.
• In multi-GPU training, fast peer-to-peer communication avoids bottlenecks and idle compute cycles.

This is where your interconnect choice matters. And depending on your setup, it could be the difference between smooth scaling or wasted GPU capacity.

What is PCIe?

PCIe (Peripheral Component Interconnect Express) is a high-speed interface that connects GPUs and other hardware components to the CPU for data transfer between devices within a system.

What is NVLink?

NVLink is NVIDIA’s high-bandwidth interconnect that enables direct GPU-to-GPU communication for faster data exchange and shared memory access between GPUs, especially in multi-GPU AI and HPC workloads.

When to Choose PCIe for AI Workloads

Here’s when you should choose PCIe GPUs for AI workloads:

• You’re Running Scalable Inference Jobs

If your workload involves serving AI models like running thousands of image classifications, chatbot responses or recommendation queries across many isolated jobs, PCIe is ideal. These tasks don’t require GPUs to talk to each other, so high-speed interconnects like NVLink aren’t necessary. 

• You’re Training Smaller Models

For early-stage experimentation or training models that comfortably fit on a single GPU setting, PCIe delivers more than enough performance. It’s a cost-effective way to iterate quickly without over-investing in high-end infrastructure. See how we used NVIDIA A100 PCIe to use Llama 3.3 70 B to experiment with the model in this tutorial.

• You Need Flexibility 

If your AI workloads run across a mix of local machines, cloud servers or others, PCIe ensures broad compatibility. It works across nearly all platforms and setups, great for teams deploying across hybrid environments or scaling AI services without infrastructure headaches.

When to Choose NVLink for AI Workloads

Here’s when you should choose NVLink GPUs for AI workloads:

• You’re Training Large Models

For instance, training a massive 671 billion parameter model like DeepSeek-R1-0528 requires powerful GPUs with NVLink. It enables high-bandwidth and low-latency communication between GPUs. We used 8xNVIDIA H100 SXM (with NVLink) GPUs to run this model on Hyperstack. Find the full tutorial here.

• You Want Faster Results

Some AI models (transformer-based architectures) can slow down if GPU memory access becomes a bottleneck. GPUs with NVLink can facilitate high-speed, low-latency data transfer between GPUs for smoother and more efficient data flow in multi-GPU systems. This reduces idle time, improves utilisation and helps you complete training cycles faster.

• You're Using High-Bandwidth Memory (HBM) Pooling

Training massive models often needs more memory than a single GPU can handle. NVLink lets GPUs share their high-bandwidth memory, so you can train with larger batch sizes, longer sequences and more complex architectures, all without running into memory limits.

Similar Read: When to Choose SXM Over PCIe GPUs for Your AI or HPC Projects

Pricing Comparison: NVLink vs PCIe on Hyperstack

Here’s how NVLink and PCIe GPUs compare on Hyperstack’s infrastructure in terms of hourly pricing:

Conclusion: Which One Should You Use?

You’re not really choosing between NVLink and PCIe, you’re choosing whether or not to add NVLink to a PCIe-connected system via SXM GPUs.

If your workload is memory-intensive, parallel and multi-GPU, go for NVLink-enabled GPUs like NVIDIA A100 SXM or NVIDIA H100 SXM.  If your workload is smaller or inference-heavy, PCIe GPUs are more than capable.

Hyperstack give you both options:

• PCIe GPUs for flexible deployment
• NVLink GPUs for performance-critical training

Choose the setup that matches your model size, training time constraints and budget.

Need help picking the right GPUs for your AI workload? Explore PCIe and NVLink GPU options on Hyperstack. 

FAQs

What is PCIe?

PCIe (Peripheral Component Interconnect Express) is a high-speed interface that connects GPUs to CPUs for data transfer within a system.

What is NVLink?

NVLink is NVIDIA’s high-bandwidth interconnect that allows fast, direct communication and memory sharing between multiple GPUs in a system.

What are the key differences between NVLink and PCIe?

NVLink delivers much higher bandwidth and lower latency than PCIe switches for faster and more efficient GPU-to-GPU communication within a server and boosting overall performance.

What is the price of the NVIDIA H100 PCIe on Hyperstack?

The price of NVIDIA H100 PCIe on Hyperstack is:

• On-demand: $1.90/hour
• Reserved: $1.33/hour

What is the price of the NVIDIA A100 PCIe on Hyperstack?

The price of the NVIDIA A100 PCIe on Hyperstack is:

• On-demand: $1.35/hour
• Reserved: $0.95/hour

When should I choose PCIe or NVLink for AI workloads?

Use PCIe for smaller models and inference; NVLink is best for multi-GPU training of large, memory-intensive AI models.

Can I use both PCIe and NVLink GPUs on Hyperstack?

Yes, Hyperstack offers both PCIe and NVLink GPU options, so you can choose based on performance needs and budget.

Why is NVLink better for large model training?

NVLink enables faster data transfer between GPUs, reducing bottlenecks during parallel training of large models like LLMs.