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

NVLink or PCIe for AI? This blog tells why NVLink excels in multi-GPU communication with higher bandwidth, while PCIe is flexible for single-node setups. Using benchmarks and real AI workloads, we show how each interconnect affects training speed, memory access, and inference efficiency. Quickly understand which setup fits your AI project requirements.

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.

Architectural Differences: How NVLink and PCIe Work 

While both PCIe and NVLink serve as interconnects that move data between GPUs and other system components, they do so in fundamentally different ways:

PCIe (Peripheral Component Interconnect Express)

• Hierarchical host-centric bus: PCIe is a general-purpose interface that connects GPUs, storage, and other peripherals to the CPU and system memory through a central switch or root complex.

• CPU-mediated paths: Communication between GPUs often travels through the CPU or chipset, introducing additional hops and overhead.

• Standardised and versatile: PCIe’s widespread ecosystem supports many device types and platforms, making it ideal for flexible deployments.

NVLink (NVIDIA High-Speed Interconnect)

• Mesh-style GPU network: NVLink provides direct, high-bandwidth, point-to-point links between GPUs without requiring every transfer to route through the CPU.

• Unified memory access: Some NVLink implementations support shared memory spaces across GPUs, reducing the need for redundant data copies and enabling GPUs to access each other’s memory more efficiently.

• Scalable multi-GPU fabric: By leveraging NVLink switches and multiple links per GPU, systems can scale to many GPUs with consistent high throughput and low latency.

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 Interconnect Should You Choose?

Choosing between NVLink and PCIe doesn’t have to be complicated — it comes down to the scale and communication patterns of your workload:

Choose NVLink when:

• You’re training large, memory-intensive models that span across multiple GPUs.

• Your workload depends on fast GPU-to-GPU communication with minimal latency.

• Maximum utilisation and throughput in multi-GPU setups matter more than the incremental cost.

Stick with PCIe when:

• You’re working with single-GPU tasks or models that fit comfortably within one GPU’s memory.

• Your primary focus is high-throughput inference rather than distributed training.

• Cost efficiency and broader hardware flexibility are key priorities.

In short: NVLink accelerates multi-GPU training by eliminating data movement bottlenecks, while PCIe delivers cost-effective performance for single-GPU workloads, inference tasks, and general-purpose deployments. Align your choice with your model size, performance needs, and budget to get the most out of your AI infrastructure.

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