What is the NVLink Speed Improvement in H100 Compared to A100

Introduction: Why Interconnect Speed Matters More Than Ever

In today’s cloud-driven world, performance is no longer defined only by how powerful a single GPU is. Instead, it’s about how efficiently multiple GPUs, servers, and cloud hosting environments communicate with each other. According to recent industry trends, over 70% of large-scale AI and HPC workloads now rely on multi-GPU or multi-node architectures, especially in cloud and hybrid cloud setups. As models grow larger and datasets become more complex, the bottleneck has shifted from raw compute to data movement.

This is exactly where NVLink comes into play.

When NVIDIA introduced the A100 GPU, NVLink was already a game-changer for GPU-to-GPU communication inside servers and data centers. But with the launch of the NVIDIA H100 (Hopper architecture), NVLink has taken a significant leap forward. The improvement is not just about higher numbers on a spec sheet—it directly impacts AI training speed, cloud hosting efficiency, server scalability, and overall workload performance.

In this blog, we’ll break down what NVLink is, how it worked in A100, what changed in H100, and most importantly, why this speed improvement actually matters for cloud, server infrastructure, and modern data centers.

Understanding NVLink: A Quick Refresher

Before diving into comparisons, let’s quickly understand NVLink in simple terms.

NVLink is NVIDIA’s high-speed interconnect technology designed to allow GPUs to communicate directly with each other at much higher speeds than traditional PCIe. In a typical server or cloud environment, GPUs often need to share data—model parameters, tensors, memory states, or intermediate results. If this data transfer is slow, even the most powerful GPU ends up waiting idle.

NVLink solves this by:

- Enabling direct GPU-to-GPU communication

- Reducing latency

- Increasing bandwidth

- Allowing memory pooling across GPUs

This is especially critical in cloud hosting environments where multiple GPUs are deployed within the same server to support AI training, machine learning inference, analytics, and HPC workloads.

NVLink in A100: Setting the Baseline

The NVIDIA A100 GPU, based on the Ampere architecture, introduced NVLink 3rd generation. At the time, it was a major step forward for data center and cloud infrastructure.

Key NVLink Capabilities in A100

- NVLink generation: 3rd Gen

- Bandwidth per NVLink: 50 GB/s (bidirectional)

- Total NVLink bandwidth per GPU: Up to 600 GB/s

- Links per GPU: 12 NVLink connections

This allowed A100-based servers to handle:

- Large-scale AI model training

- High-performance computing simulations

- Memory-intensive workloads across multiple GPUs

For many cloud providers and enterprises, A100 became the backbone of AI-focused cloud hosting platforms. However, as AI models crossed billions and even trillions of parameters, the demand for even faster interconnects became unavoidable.

NVLink in H100: What Actually Improved?

The NVIDIA H100 GPU introduces NVLink 4th generation, and this is where the real leap happens.

NVLink Improvements in H100

- NVLink generation: 4th Gen

- Bandwidth per NVLink: 100 GB/s (bidirectional)

- Total NVLink bandwidth per GPU: Up to 900 GB/s

- Links per GPU: 18 NVLink connections

Speed Comparison: A100 vs H100

In simple terms:

- Per-link bandwidth doubled

- Total GPU-to-GPU bandwidth increased by ~50%

- More GPUs can communicate faster and more efficiently

This improvement is massive for modern cloud and server architectures.

Why NVLink Speed Matters in Real-World Cloud Workloads

At first glance, going from 600 GB/s to 900 GB/s may sound like a technical upgrade meant only for engineers. In reality, this directly affects business outcomes in cloud hosting and data centers.

Faster AI Training at Scale

AI training is highly communication-intensive. Large models constantly exchange gradients and parameters across GPUs. With faster NVLink in H100:

- Training time is reduced

- GPUs spend less time waiting for data

- Cloud resources are used more efficiently

For companies running AI workloads on cloud platforms, this translates into lower training costs and faster time-to-market.

Better Multi-GPU Server Utilization

In traditional setups, GPUs often become underutilized due to slow interconnects. H100’s improved NVLink ensures:

- Balanced workload distribution

- Higher throughput per server

- Better ROI on expensive GPU servers

This is especially important for cloud providers offering GPU-based cloud hosting services.

NVLink and NVSwitch: Scaling Beyond a Single Server

Another major advantage of H100’s NVLink improvement is how well it integrates with NVSwitch.

NVSwitch allows all GPUs in a server to communicate with each other at full NVLink speed. With H100:

- Large GPU clusters can act like a single massive GPU

- Memory sharing across GPUs becomes more efficient

- Communication overhead drops significantly

In cloud environments, this enables:

- Larger instance sizes

- More powerful AI and HPC offerings

- Better performance isolation for enterprise customers

This is one of the reasons many modern cloud data centers are redesigning their server architecture around H100-based systems.

Impact on Cloud Hosting and Data Center Design

The NVLink speed improvement in H100 doesn’t just benefit workloads—it influences how servers and cloud infrastructure are designed.

Higher Density, Better Performance

With faster interconnects:

- Fewer servers are needed to achieve the same performance

- Data centers can reduce power and cooling overhead

- Cloud hosting platforms can offer premium GPU instances

Improved Scalability

As workloads scale horizontally across GPUs and nodes, fast NVLink reduces performance degradation. This makes H100 ideal for:

- Distributed AI training

- Large-scale simulations

- Data-intensive analytics

For enterprises moving to cloud or hybrid cloud setups, this scalability is a major advantage.

A100 vs H100: Is NVLink the Deciding Factor?

While H100 brings several improvements (Tensor Cores, Transformer Engine, better FP8 support), NVLink speed is one of the most impactful changes for multi-GPU environments.

If your workload:

- Runs on a single GPU

- Is not communication-heavy

Then the difference may feel incremental.

But if you are:

- Running AI training in the cloud

- Using multi-GPU servers

- Operating large-scale data center workloads

Then the NVLink improvement alone can justify the move from A100 to H100.

Conclusion: NVLink as the Backbone of Next-Gen Cloud Performance

The NVLink speed improvement in H100 compared to A100 is not just an upgrade—it’s a response to how modern workloads actually behave. By doubling per-link bandwidth and increasing total GPU-to-GPU throughput to 900 GB/s, NVIDIA has addressed one of the biggest bottlenecks in AI, cloud hosting, and server-based computing.

For cloud providers, this means offering faster, more scalable GPU instances. For enterprises, it means better performance, reduced training time, and improved efficiency. And for data centers, it means designing smarter, denser, and more cost-effective infrastructure.

As AI models continue to grow and cloud workloads become more interconnected, NVLink is no longer a “nice-to-have” feature—it’s a critical backbone. With H100, NVIDIA has clearly set the direction for where high-performance cloud and server computing is headed next.