What Are Scale-Up and Scale-Out in AI Data Centers? A Complete Guide to GPU High-Speed Network Architecture｜Technology Year：2022

As AI servers scale from a few GPUs to hundreds or even thousands of GPUs, system performance is no longer limited by GPU compute power alone.

Another critical factor is:

How efficiently can GPUs exchange data with each other?

This is why modern AI infrastructure commonly uses two major networking architectures:

Scale-Up Network
Scale-Out Network

The simplest distinction is:

Scale-Up connects multiple GPUs within a tightly coupled compute domain using extremely high bandwidth and low latency.

Scale-Out connects multiple servers, racks, or GPU domains through a high-speed network fabric to build a larger AI cluster.

Technologies such as NVLink and NVSwitch are representative Scale-Up implementations, while InfiniBand and AI-optimized Ethernet are commonly used for Scale-Out networking.

Scale-Up vs Scale-Out: Quick Comparison

The boundary should not be defined only by the number of servers.

Modern rack-scale AI systems can extend a Scale-Up domain across an entire rack.

A more accurate interpretation is:

Scale-Up describes whether GPUs belong to the same tightly coupled high-speed compute domain, while Scale-Out describes how multiple compute nodes or domains are interconnected.

Why Does Scale-Up Require Extremely High Bandwidth and Low Latency?

Large AI models often require many GPUs to process the same workload together.

These GPUs may frequently exchange:

• Tensor data
• Model parameters
• Intermediate results
• Collective communication data
• Memory data

If the GPUs can compute quickly but spend too much time waiting for data from other GPUs, overall AI training or inference performance will not scale efficiently.

Scale-Up fabrics therefore prioritize:

**High Bandwidth

• Predictable Low Latency
• High GPU-to-GPU Communication Efficiency**

NVLink and NVSwitch are representative examples.

NVLink provides high-speed GPU-to-GPU connectivity, while NVSwitch can connect multiple NVLink paths to create a larger all-to-all GPU communication domain.

This allows multiple accelerators to operate more like one tightly integrated compute system rather than a collection of isolated GPUs.

What Does Scale-Out Do?

Once a single Scale-Up domain is no longer large enough, additional servers or racks must be connected.

That is the role of:

Scale-Out

A simplified architecture looks like:

GPU Domain
→ NIC / SuperNIC / DPU
→ Scale-Out Switch Fabric
→ NIC / SuperNIC / DPU
→ Another GPU Domain

Scale-Out must manage more than a single high-speed link.

It operates as a complete network fabric and therefore must handle:

• Network topology
• Multipath
• Congestion control
• Packet loss
• Collective communication
• Load balancing
• Fault recovery
• Large-scale network expansion

Common Scale-Out technologies include:

InfiniBand
Ethernet-Based AI Networking

The detailed differences between Ethernet and InfiniBand involve a separate search intent and should be evaluated independently in terms of protocol, RDMA, congestion control, ecosystem, scalability, and cost.

Why Can't AI Data Centers Use Scale-Up Only?

At first glance, directly connecting every GPU with an ultra-high-bandwidth Scale-Up fabric may sound ideal.

However, as GPU count grows, the system quickly encounters limits in:

**Switch Ports

• SerDes
• Cables
• Connectors
• Power
• Cooling
• Physical Reach
• Cost**

Scale-Up fabrics are designed for extremely high bandwidth density and low latency, making them most effective within a relatively tightly coupled physical domain.

They are not intended to scale without limit across an entire data center.

Large AI infrastructure therefore typically follows a hierarchical architecture:

Scale-Up Domain
↓
Scale-Out Network
↓
More Scale-Up Domains

In other words:

Scale-Up builds powerful GPU compute domains, while Scale-Out connects those domains into a much larger AI cluster.

How Do Scale-Up and Scale-Out Affect Cable and Connector Requirements?

Both architectures depend on high-speed cables and connectors, but their engineering priorities are different.

Scale-Up systems often emphasize:

Short-Reach High-Speed Copper
High-Density Connectors
Low-Loss Channels
Flyover Cables
Backplane / Midplane Architectures

because GPU-to-GPU communication must maintain very high bandwidth with minimal latency.

Scale-Out links cover a wider range of distances, so the transmission medium may change according to reach:

DAC
→ AEC
→ AOC
→ Optical Transceiver + Fiber

Therefore, AI data center cable selection should not begin only with:

“Do we need 400G or 800G?”

The system designer should first determine whether the connection belongs to:

Scale-Up

or:

Scale-Out

because the physical and electrical requirements can be very different.

Why Does Scale-Out Become More Important as GPU Count Increases?

Large AI clusters generate enormous amounts of synchronized East-West traffic.

Traditional data centers historically handle large amounts of:

North-South Traffic

between users and servers.

AI training, however, creates large volumes of communication between:

GPU ↔ GPU
Server ↔ Server
Rack ↔ Rack

This East-West traffic can become one of the main performance bottlenecks.

A Scale-Out fabric must therefore prevent expensive GPUs from waiting on the network due to:

• Congestion
• Packet loss
• Tail latency
• Uneven routing
• Slow collective communication

The key metrics are no longer limited to port speed.

Modern AI networking must consider:

**Bandwidth

• Latency
• Congestion Control
• Collective Communication
• Network Topology
• Reliability**

as a complete system.

Which Is More Important: Scale-Up or Scale-Out?

Both are essential.

They solve different problems.

Scale-Up answers:

How can multiple GPUs within a limited physical domain work together as one highly efficient compute system?

Scale-Out answers:

How can additional servers, racks, and GPU domains be connected to build a much larger AI cluster?

A simplified large-scale AI architecture is therefore:

GPU
→ Scale-Up Fabric
→ GPU Domain
→ Scale-Out Fabric
→ AI Cluster

rather than choosing one architecture over the other.

What Is Scale-Across?

A newer AI infrastructure term is:

Scale-Across

It generally describes interconnecting AI infrastructure across:

Multiple Data Centers
Buildings
Campuses
Geographically Distributed AI Factories

The three dimensions can be simplified as:

Scale-Up: GPU Domain

Scale-Out: Data Center Cluster

Scale-Across: Multiple Data Centers

Scale-Across is becoming more relevant as AI infrastructure expands beyond the capacity of a single data center.

However, for most AI server and data center hardware architectures, Scale-Up and Scale-Out remain the two most fundamental networking layers.

Scale-Up vs Scale-Out: Key Takeaways

The four most important points are:

First, Scale-Up builds a tightly coupled GPU compute domain using extremely high bandwidth and low latency.

Second, Scale-Out uses a high-speed network fabric to connect additional servers, racks, and GPU domains into a large AI cluster.

Third, Scale-Up does not mean NVLink only, and Scale-Out does not mean InfiniBand only. These are representative technology implementations rather than universal definitions.

Fourth, large AI data centers require Scale-Up and Scale-Out to work together rather than choosing one over the other.

A complete AI infrastructure network architecture should therefore evaluate:

**GPU Architecture

• Scale-Up Fabric
• Scale-Out Fabric
• NIC / DPU
• Switch
• Cable / Connector
• Reach
• Signal Integrity
• Power / Cooling**

as one integrated system.

SyncTronics Solutions

High-speed interconnect design for AI servers and data centers should be evaluated according to Scale-Up and Scale-Out architecture, lane rate, port type, cable length, Signal Integrity, and equipment configuration. For short-reach rack-level links, high-speed copper cables, DAC, AEC, and high-density Cable Assemblies can be evaluated according to system requirements. For rack-to-rack or longer-distance connections, AOC, optical transceivers, and fiber architectures may become more suitable. SyncTronics provides specification verification and customized interconnect evaluation for server, networking, AI infrastructure, and data center applications.

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