Scale-up networking links accelerators or other compute elements inside one tightly coupled domain so they can exchange data with very high bandwidth and very low latency. That domain may be a server, a rack, or—depending on the architecture—several closely connected racks. Scale-out networking connects multiple servers or scale-up domains across racks, allowing a workload to grow to more nodes.
The two approaches are complementary rather than competing definitions. Scale-up optimizes locality and close coordination; scale-out provides reach, capacity and growth. A modern AI cluster commonly uses both, with separate fabrics for accelerator traffic, inter-rack communication, storage, management and external access.
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What scale-up networking means
Scale-up networking is the communication fabric inside a tightly coupled compute domain. Its job is to make multiple accelerators behave as a coordinated system instead of as isolated devices. The links and switches are designed for frequent, large transfers, synchronized operations and collective traffic between processors.
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Why locality matters
Training and inference workloads often exchange model parameters, activations, gradients or intermediate results repeatedly. Every extra hop, queue and software boundary can add delay. A scale-up fabric therefore emphasizes short paths, high delivered bandwidth, predictable latency and efficient collective operations such as all-to-all exchanges.
Dense or single-tier topologies are common because they reduce distance and hop count, although no single topology defines scale-up. The physical design can include tightly integrated links, optical or copper interconnects, and switches built for accelerator traffic. In Ethernet-based systems, multi-ASIC switching and changes to the protocol and software stack may be required to achieve the intended behavior.
What is inside a scale-up domain
- Accelerators such as GPUs or other specialized compute devices.
- High-speed links and switches connecting those devices.
- Network interface components, cables or optics matched to the fabric.
- Software and collective-communication support that exposes the connectivity to applications.
- Power, cooling and serviceability designed around a dense, tightly coupled system.
The important measurement is not a link’s advertised rate in isolation. Accelerator-to-accelerator latency, sustained bandwidth under the intended traffic pattern, collective-operation performance and behavior during congestion determine whether the domain actually improves application performance.
What scale-out networking means
Scale-out networking joins multiple nodes or scale-up domains into a larger cluster. It normally crosses server and rack boundaries and can grow to many racks or a broader data-center fabric. Instead of making one domain tighter, it makes the overall system wider.
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Scale-out must carry node-to-node and rack-to-rack traffic while the cluster grows. That creates different engineering pressures: aggregate bandwidth, routing, congestion control, traffic balance, fault isolation and the ability to add capacity without redesigning the entire fabric. Multi-stage topologies, often Clos-like, are common because they provide a repeatable way to connect many endpoints.
Communication patterns change at cluster scale
A distributed job may perform collectives across nodes, synchronize checkpoints, exchange data between pipeline stages or send requests to remote inference workers. The fabric must sustain those patterns without allowing a few oversubscribed links or queues to dominate completion time. Tail latency—the slowest portion of an operation—can matter as much as the average.
Scale-out also has a larger operational surface. More switches, links, optics, software components and failure domains mean more opportunities for a fault or configuration mismatch. A design must specify how traffic is rerouted, how a failed component is isolated and how the cluster continues operating while hardware is repaired.
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Scale-up and scale-out compared
| Axis | Scale-up | Scale-out |
|---|---|---|
| Main purpose | Couple accelerators into one compute domain | Connect multiple nodes or domains into a cluster |
| Typical scope | Inside a server, rack or tightly coupled group of racks, depending on the system | Across servers and racks, potentially across a larger data-center fabric |
| Primary workload pressure | Low latency, high bandwidth, close coordination and memory or collective traffic | Aggregate bandwidth, cluster reach, node-to-node collectives and congestion management |
| Common topology tendency | Dense or single-tier fabric intended to reduce distance and hop count | Multi-stage fabric, often Clos-like, intended to expand node count |
| Main trade-off | Domain size is constrained by the system and interconnect architecture | More nodes and reach add fabric complexity and may increase communication overhead |
| How to evaluate it | Delivered accelerator-to-accelerator performance and software behavior | End-to-end application performance, scale, utilization, failure handling and operations |
These are tendencies, not mandatory definitions. A vendor can extend a scale-up domain across multiple racks, and a particular scale-out fabric may deliver very low latency. Generation, topology, configuration and measurement method all affect the result.
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How the two fabrics work together
A useful mental model is a hierarchy. The scale-up layer handles the most frequent and latency-sensitive exchanges among accelerators that need to act as one engine. The scale-out layer connects those domains so a job can use more compute than one domain contains.
NVIDIA describes the distinction this way: “Scale-out networks connect servers across the data center. Scale-up networks enable the GPUs inside the domain to behave as a single engine of compute.” The wording is vendor guidance, not a formal industry standard, but it captures the architectural split.
A documented rack-scale example
The NVIDIA DGX GB Rack Scale Systems User Guide describes a rack containing 72 GPUs. In that system, NVLink is the memory-coherent scale-up fabric within the rack, while InfiniBand provides scale-out compute connectivity between racks. Ethernet handles storage, management and external connectivity.
This example shows why “the network” can mean several separate fabrics in one AI installation. It does not mean that every scale-up system uses NVLink, that every scale-out system uses InfiniBand, or that a rack normally contains 72 GPUs. Other architectures can choose different protocols, switch arrangements and boundaries.
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Latency figures: useful context, not universal limits
The SONiC project’s Ethernet Scale Up AI Cluster Architecture, version 1.0 dated July 27, 2026, presents illustrative round-trip-time figures of less than 2 microseconds for scale-up and less than 20 microseconds for scale-out. Those values belong to that document’s comparison table. They are not universal definitions, guaranteed limits or an apples-to-apples benchmark across vendors and workloads.
| Traffic role | Illustrative RTT in the SONiC 2026 architecture document | How to interpret it |
|---|---|---|
| Scale-up | Less than 2 microseconds | Document-specific illustrative target for tightly coupled traffic |
| Scale-out | Less than 20 microseconds | Document-specific illustrative target for inter-node or inter-rack traffic |
Actual delivered latency depends on the generation of the equipment, hop count, packet size, congestion, routing, software and the way the measurement is taken. A lower quoted RTT does not automatically produce faster training or inference if collective operations, queueing or application synchronization remain bottlenecks.
How to evaluate a real design
Start with the workload, then map the workload to the two networking layers. Use this process before selecting a protocol, switch or topology.
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- Describe the traffic. Identify whether the job performs frequent collectives, tight synchronization, large streaming transfers, storage access, parameter exchange or mostly independent requests. Frequent accelerator-to-accelerator traffic points to a strong local scale-up domain; communication that spans nodes and racks requires a scale-out fabric designed for the relevant pattern.
- Draw the domain boundary. Record which accelerators must communicate as one group and where the design places the boundary: server, rack or multiple racks. Do not assume a rack count defines scale-up or scale-out for every vendor.
- Measure delivered performance. Test accelerator-to-accelerator bandwidth and end-to-end latency under representative message sizes and concurrency. Include collective operations, not only point-to-point link tests. Report the workload, topology and configuration with every result.
- Inspect the topology. Count hops, identify oversubscribed links and examine how traffic is balanced. For scale-out, verify that the multi-stage fabric has enough paths and capacity for the expected all-to-all or many-to-many exchanges.
- Test congestion and failure behavior. Observe queueing and tail latency when several jobs run simultaneously. Then model or test a failed link, switch or endpoint: determine how quickly traffic reroutes, what capacity remains and whether the application continues.
- Verify software integration. Confirm that accelerators, network interfaces, switches, drivers, communication libraries and orchestration software support the chosen fabric together. A fast physical link is not useful if the software cannot use collective operations efficiently.
- Plan growth. Document the number of nodes and racks the fabric can support, how additional capacity is added and which components become bottlenecks first. Scale-out should grow without forcing a complete topology replacement.
- Include operations and cost. Compare power, cooling, cabling, optics, serviceability, spares, monitoring and maintenance alongside purchase cost. NVIDIA’s technical guidance emphasizes that all-to-all fabric bandwidth, end-to-end latency, in-network collectives, integrated software and operational reliability all affect delivered results.
Common mistakes and how to avoid them
Calling every larger system scale-up
Adding more processors to a server does not automatically create a useful scale-up domain. The devices need an interconnect and software path that support the required coordination. Conversely, a switched fabric can make a multi-rack group function as one scale-up domain.
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Comparing peak bandwidth alone
Peak port rate or aggregate switch capacity says little about application completion time when traffic is collective, bursty or congested. Compare sustained, end-to-end behavior with the software stack enabled.
Treating protocols as definitions
NVLink, InfiniBand and Ethernet are implementation choices, not synonyms for scale-up or scale-out. NVIDIA’s rack example assigns different jobs to those fabrics, while other vendors can use different combinations. Evaluate the architecture and workload rather than inferring the role from a protocol name.
Assuming the rack is always the boundary
Rack boundaries are convenient physical references, but they do not define the architecture. Marvell describes scale-up as connectivity that couples accelerators into a single compute domain within a rack today and increasingly across several racks. The exact boundary depends on switching, interconnect, software and system design.
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Data-center accelerator fabrics require exact port speeds, buffering, congestion behavior, topology and software compatibility. A general office or home switch is not interchangeable with a purpose-built accelerator-fabric switch.
Ignoring serviceability
A design that delivers excellent results only while every component is healthy may be unsuitable for a production cluster. Check replacement procedures, remaining capacity after a failure, monitoring and the effect of maintenance on running jobs.
When each approach matters most
Favor scale-up strength when
- Accelerators exchange data constantly inside one coordinated job.
- Collective operations and synchronization dominate runtime.
- The application is sensitive to microsecond-level latency or uneven tail behavior.
- The required compute fits within one tightly coupled domain.
Favor scale-out capacity when
- The job needs more accelerators than one domain provides.
- Multiple training, inference or data-processing services must share a cluster.
- Capacity must grow incrementally across servers and racks.
- Workloads can tolerate, or the fabric can control, additional inter-node communication overhead.
Most large AI systems need both choices: a high-performance local domain and a scalable fabric between domains. The right balance depends on how much communication the application generates and how the system must grow and operate.
Bottom line
Scale-up networking makes a group of accelerators behave like one tightly coordinated compute engine; scale-out networking connects those groups into a larger, expandable cluster. Scale-up is chiefly a locality and coordination problem, while scale-out is a reach, capacity and fabric-operations problem. Judge either design by delivered application performance, collective behavior, congestion, failure handling, software integration and serviceability—not by a headline bandwidth number or a protocol name.
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