What makes AI data center networks different from traditional data centers?

AI data center networks are built for massively parallel processing and data movement between tightly coupled GPUs. Unlike traditional enterprise or cloud networks, which typically optimize for north-south traffic and client-server models, AI networks must support high-throughput, low-latency east-west traffic across high-radix switch fabrics. AI training workloads involve all-to-all and many-to-many communication, requiring precise synchronization across thousands of nodes. This results in traffic patterns that are bursty, non-uniform, and highly sensitive to congestion. Standard validation tools often fall short because they lack the ability to simulate the complex, large-scale workloads typical in AI/ML environments. Keysight AI Data Center Builder is purpose-built for this space because it models the physical and logical topology, emulates realistic AI workloads, and exposes system-wide performance bottlenecks before hardware deployment.

How do I simulate AI workloads for data center validation?

Accurately simulating AI workloads requires traffic emulation that mirrors the communication behavior of training and inference jobs. AI training generates high-volume, bursty, synchronized traffic. These flows stress the network differently than traditional workloads and can overwhelm switch buffers, trigger head-of-line blocking, or saturate specific links. Keysight AI Data Center Builder allows you to generate these exact traffic patterns at scale, across synthetic GPU endpoints, programmable switches, and fabric interconnects. It enables you to model job arrival rates, burst sizes, and synchronization timing to provide a realistic, repeatable way to measure system behavior under representative AI/ML conditions. This workload emulation is essential for early-stage validation of topology, routing, and scheduling strategies in high-performance data center environments.

Why is congestion testing important in AI data center design?

AI workloads demand high link utilization and tight synchronization across GPU clusters. Any fabric-level congestion (tail latency) can introduce queuing delays or packet drops, which in turn cause GPU stalls and degraded training performance. Because many AI frameworks rely on collective communication, the slowest node in the network can bottleneck the entire job. Traditional performance tests often miss these interactions. Keysight’s emulation-based congestion analysis provides end-to-end visibility into traffic flow, hot spots, and congestion propagation. You can test different topologies, simulate real-time contention, and evaluate how queue buildup affects latency and throughput. The result is a deeper understanding of how topology design, routing algorithms, and buffer strategies impact overall system efficiency before committing to hardware or deploying workloads.

How can I benchmark AI data center performance during development?

Effective benchmarking of AI data center infrastructure requires system-level testing that mirrors real-world AI communication patterns. This includes evaluating end-to-end latency, throughput, and congestion behavior across compute nodes, switch fabrics, and interconnect links. With Keysight AI Data Center Builder, you can perform controlled, repeatable performance benchmarks using application-level traffic emulation. The platform supports testing of scaling behavior (e.g., how performance changes with more GPUs), stress testing with multiple concurrent jobs, and measurement of critical KPIs such as job completion time, packet loss, and congestion hotspots. By benchmarking different switch vendors, link technologies, or topologies, teams can make data-driven architectural decisions. These insights help optimize fabric design for training efficiency, minimize underutilization, and accelerate the deployment of scalable AI infrastructure.

What’s the benefit of using emulation tools for AI data center planning?

Emulation tools like Keysight AI Data Center Builder enable proactive planning and validation of AI network infrastructure without needing physical GPUs or switches in place. They let you create digital twins of your data center topology, simulate realistic AI job workloads, and analyze system performance under varying traffic conditions. This virtualized approach allows you to identify design flaws, evaluate alternative architectures, and tune routing and scheduling strategies before hardware is deployed. By exposing performance bottlenecks and congestion risks early, emulation reduces costly design iterations and minimizes the risk of inefficiencies in live environments. For teams planning GPU cluster expansion, topology migration, or next-gen switch deployments, emulation provides a scalable, low-risk way to ensure readiness and performance at scale.

AI Data Center Builder

Emulate AI workloads to optimize data center performance and reliability

Keysight AI (KAI) Data Center Builder is an emulation solution that helps you plan and validate your AI infrastructure before you deploy it. Model network architectures, simulate realistic workloads, and test throughput and congestion-handling scenarios across compute nodes, switches, and interconnects. KAI Data Center Builder gives you visibility into system-level performance, enabling smarter design choices to maximize efficiency and reliability. Whether you are validating new topologies or evaluating network upgrades, KAI Data Center Builder helps you do it faster, at lower risk, and with greater insight. Select a model bundled with a hardware accelerator, or choose a software-only option. Request a quote for one of our popular configurations today. Need help selecting? Check out the resources below.

Workload emulation

Simulate AI training and inference traffic patterns to evaluate how your network handles realistic, bursty workloads across GPUs, switches, and interconnects.

Performance benchmarking

Run system-level benchmarks to assess the impact of AI model parameters, network parameters, and their interactions at scale.

Data flow visualization

Analyze tail latency, identify bottlenecks, and learn how topology and component decisions impact performance across the entire system.

Network optimization

Identify design flaws early to reduce the number of physical test iterations, accelerating validation and reducing time to deployment.

Protocol support

RDMA, RoCEv2
Collective operations supported

All gather (unidirectional ring), All reduce (bidirectional ring; halving doubling; unidirectional ring), All to all (parallel; PXN), Broadcast (parallel), Gather (parallel), Reduce scatter (unidirectional ring), All reduce, All gather, Reduce scatter, All to all, Broadcast, Gather, Unidirectional ring, Bidirectional ring, Halving-doubling all reduce, Parallel, PXN all to all
Compatible hardware

AresONE 1600, AresONE-S, AresONE-M, RDMA NIC
Applications

Collective Benchmarks, Workload Emulation, IxNetwork
License types

Software Bundle, Application, Transport Endpoint, Hardware Bundle, Perpetual
Ports

4 to 16
Interface

QSFP-DD, QSFP-DD800, OSFP

Most popular configurations

KAI Data Center Builder Start Bundle with Collective Benchmarks Application

Model

950-2001

Protocol-support

-

Collective-operations-supported

-

Compatible-Hardware

-

Protocol support

RDMA, RoCEv2

Collective operations supported

All gather (unidirectional ring), All reduce (bidirectional ring; halving doubling; unidirectional ring), All to all (parallel; PXN), Broadcast (parallel), Gather (parallel), Reduce scatter (unidirectional ring)

Compatible hardware

AresONE 1600, AresONE-S, AresONE-M, RDMA NIC

KAI Data Center Builder Workloads bundle with Collective Benchmarks and Workload Emulation Applications

Model

950-2002

Protocol-support

-

Collective-operations-supported

-

Compatible-Hardware

-

Protocol support

RDMA, RoCEv2

Collective operations supported

All gather (unidirectional ring), All reduce (bidirectional ring; halving doubling; unidirectional ring), All to all (parallel; PXN), Broadcast (parallel), Gather (parallel), Reduce scatter (unidirectional ring)

Compatible hardware

AresONE 1600, AresONE-S, AresONE-M, RDMA NIC

KAI Data Center Builder with Collective Benchmarks Application

Model

950-2101

Protocol-support

-

Collective-operations-supported

-

Compatible-Hardware

-

Protocol support

RDMA, RoCEv2

Collective operations supported

All gather (unidirectional ring), All reduce (bidirectional ring; halving doubling; unidirectional ring), All to all (parallel; PXN), Broadcast (parallel), Gather (parallel), Reduce scatter (unidirectional ring)

Compatible hardware

AresONE-S, AresONE-M, RDMA NIC

Select the KAI Data Center Builder that is right for you

The resources below will help you determine the test platform you need.

Explore resources

Explore Our Ethernet Traffic Emulator Catalog
How AI / ML Networks Differ from Traditional Networks
How to Test AI Data Center Networks

Services and support

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Keysight Support Portal

Experience elevated service as a KeysightCare subscriber to get committed technical response and more.

Calibration

Ensure your test system performs to specification and meets local and global standards.

Education

Make measurements quickly with in-house, instructor-led training, and eLearning.

Software Download Center

Download Keysight software or update your software to the newest version.

Keysight AI Data Center Builder Accelerate design and deployment of AI network infrastructure