What are the Key Differences Between NVLink and PCIe?

Vishnu Subramanian

Founder @JarvisLabs.ai

NVLink offers dramatically higher bandwidth (up to 900 GB/s) and lower latency compared to PCIe Gen 5 (128 GB/s), making it superior for multi-GPU AI workloads. However, PCIe provides universal compatibility and cost-effectiveness for general-purpose computing.

Bandwidth and Performance

The most significant difference between NVLink and PCIe lies in their bandwidth capabilities:

NVLink 4.0 (H100/H200): Up to 900 GB/s per GPU with 18 bidirectional links NVLink 3.0 (A100): Up to 600 GB/s per GPU with 12 bidirectional links PCIe Gen 5: Up to 128 GB/s for x16 configuration (32 GT/s per lane) PCIe Gen 4: Up to 64 GB/s for x16 configuration (16 GT/s per lane)

NVLink provides more than 7x the bandwidth of PCIe Gen 5, making it ideal for memory-intensive AI workloads where data must move rapidly between GPUs.

Architecture Differences

NVLink Architecture:

• Direct GPU-to-GPU mesh networking
• Point-to-point connections with multiple links per GPU
• Proprietary NVIDIA technology
• CPU-GPU connectivity (on compatible platforms like IBM POWER)

PCIe Architecture:

• Hub-based system through CPU/chipset
• Industry-standard interface
• Universal compatibility across vendors
• Hierarchical tree structure

Unlike PCI Express, NVLink devices use mesh networking to communicate instead of a central hub, enabling more efficient multi-GPU communication patterns.

Latency Comparison

NVLink delivers significantly lower latency for GPU-to-GPU communication:

NVLink sports 5x the energy efficiency of PCIe Gen 5, consuming just 1.3 picojoules per bit, making it more power-efficient for high-bandwidth workloads.

Real-World Performance Impact

Based on empirical testing with Tesla P100 GPUs:

NVLink Performance:

• GPU-to-GPU bandwidth: ~35 GB/s (from 40 GB/s theoretical)
• Cross-CPU GPU communication: ~20 GB/s
• Host-to-device bandwidth: ~33 GB/s

PCIe Performance:

• GPU-to-GPU bandwidth: ~10 GB/s
• Host-to-device bandwidth: ~11 GB/s

NVLink enables 2-3x higher bandwidth compared to PCIe for GPU-to-GPU transfers, which directly translates to faster training times for large AI models.

Cost Considerations

NVLink:

• Higher hardware costs due to specialized SXM modules
• Limited to NVIDIA's high-end datacenter GPUs (H200, H100, A100, V100)
• Requires compatible server platforms

PCIe:

• Lower hardware costs
• Standard across all GPU tiers
• Wide ecosystem of compatible components

JarvisLabs GPU Options

JarvisLabs offers both NVLink and PCIe-connected GPUs:

When to Choose NVLink

Choose NVLink for:

• Multi-GPU AI training: When you need maximum bandwidth between GPUs
• Large model inference: For models requiring GPU memory pooling
• HPC workloads: Scientific computing with heavy inter-GPU communication
• Real-time applications: Where low latency is critical

When to Choose PCIe

Choose PCIe for:

• Single-GPU workloads: When inter-GPU communication isn't needed
• Budget constraints: For cost-effective AI development
• General-purpose computing: Gaming, content creation, moderate AI tasks
• Broad compatibility: When working with diverse hardware ecosystems

Future Outlook

Fifth-generation NVLink offers 1.8 TB/s bandwidth—2X more than the previous generation and over 14X the bandwidth of PCIe Gen5. Meanwhile, PCIe 6.0 is under development to further close the gap.

For most practitioners, the choice comes down to workload requirements. If you're training large models or need maximum multi-GPU performance, NVLink's bandwidth advantage justifies the higher cost. For development work or smaller models, PCIe provides excellent value with universal compatibility.

Key Takeaway

While NVLink dominates in bandwidth and latency for multi-GPU setups, PCIe remains the versatile choice for broader applications. Consider your specific workload patterns, budget, and scalability requirements when choosing your GPU interconnect strategy.