In the age of exploration, brave adventurers set sail to discover new lands, charting unknown territories and uncovering hidden treasures. Today, AI infrastructure engineers, architects, and developers are modern explorers, navigating the uncharted waters of high-performance computing to harness the full potential of artificial intelligence. The journey is perilous—AI models evolve at breakneck speed, demanding unprecedented computational power, efficiency, and scalability. To succeed, these pioneers need the right tools to chart their course, anticipate obstacles, and optimize their AI landscapes.
The challenge of AI infrastructure exploration
Imagine yourself as a network engineer on a grand expedition, setting out to optimize an AI data center. The journey is fraught with unpredictable terrain—AI workloads shift dynamically, network congestion lurks like unseen reefs, and system inefficiencies threaten progress. Your mission is to ensure that every component—GPUs, network fabrics, and algorithms—works in harmony. But how can you navigate this complexity and experiment with new configurations without risking costly failures in production environments? Keysight AI Data Center Builder (KAI DC Builder) is your trusted compass, guiding AI operators through the maze of AI infrastructure design and optimization. KAI DC Builder enables users to create detailed network diagrams, such as the one depicted in Figure 1, to optimize network design and performance.
Mapping the AI landscape with workload emulation
Just as explorers relied on maps to chart their journeys, AI engineers need a clear view of how workloads interact within their data centers. The new Workload Emulation application in KAI DC Builder allows AI operators to:
- Validate AI infrastructure performance by emulating real-world workloads
- Assess improvements from new algorithms, components, and protocols
- Optimize AI workloads and infrastructure without the expense of large-scale deployments
As shown in Figure 2 of KAI DC Builder, different data chunk sizes exhibit varying completion times, impacting overall workload performance. By visualizing cumulative distribution and detailed transmission metrics, KAI DC Builder enables engineers to fine-tune data center networking for AI workloads. With the ability to emulate Large Language Models (LLMs) like GPT and Llama, AI operators can predict how infrastructure decisions impact model training, ensuring a smoother path to greater efficiency and scalability.
Unraveling the secrets of AI training performance
In the realm of AI, model partitioning strategies determine how effectively workloads are distributed. The alignment between model partitioning, AI cluster topology, and network configuration can mean the difference between a smooth voyage and a shipwreck. KAI DC Builder helps AI explorers answer key questions about data movement efficiency, reducing bottlenecks that slow down model training. With Workload Emulation, AI operators can:
- Experiment with parallelism parameters like partition sizes and distribution
- Identify congestion points that cause inefficiencies in training completion time
- Gain insights into network utilization, tail latency, and collective operation performance
Bringing real-world AI workloads into the lab
Ancient explorers tested their navigation skills in controlled conditions before setting sail into the unknown. Similarly, AI operators, GPU cloud providers, and infrastructure vendors can now bring realistic AI workloads into lab environments, refining their designs before large-scale deployment. As seen in Figure 3, KAI DC Builder enables rigorous experimentation with model partitioning schemas, workload parameters, and AI algorithms, co-tuning infrastructure for peak performance.
Additionally, as production AI clusters scale beyond lab capacities, the Keysight AresONE test engine enables testing of scale-out AI fabric ports, ensuring that smaller AI building blocks work seamlessly before full-scale deployment.
Benchmarking the AI cluster network: The backbone of exploration
Unlike general-purpose data centers, AI clusters are dedicated to a single, high-speed data exchange—Remote Direct Memory Access (RDMA) communication between GPUs over advanced networking fabrics. Just as a well-maintained fleet ensures successful voyages, optimizing AI network performance is crucial for efficient training. The Keysight AI Data Center Collective Benchmarking application allows engineers to:
- Benchmark fabric performance for AI workloads without the need for GPUs
- Simulate collective communications (AllReduce, AllGather, ReduceScatter, AlltoAll) used in model training
- Use high-density traffic generators, like the Keysight AresONE, to replicate GPU-based data exchanges with measurable accuracy
Ensuring high-fidelity AI infrastructure emulation
Explorers relied on precise star maps to navigate treacherous waters. Similarly, the KAI DC Builder ensures accurate emulation of real AI infrastructure by matching real-world xCCL, NIC, and RoCEv2 configurations with reference benchmarks. This data-driven approach ensures fidelity, giving AI engineers the confidence to make informed decisions.
Expanding the horizon: AI workload emulation and impairments
Not every journey goes as planned—storms, miscalculations, and unforeseen obstacles can derail even the best-prepared explorers. Likewise, AI infrastructure faces real-world impairments like packet loss, buffer overflow, and host I/O backpressure. As seen in Figure 4, the KAI DC Builder Workload Emulation app allows AI architects to model these non-ideal conditions, stress-test their networks, and fine-tune their configurations.
Mastering the art of collective operations
In AI training, collective communications are the lifeblood of efficient data processing. Just as explorers optimized their routes for speed and efficiency, AI engineers must fine-tune collective operations to minimize bottlenecks. KAI DC Builder’s Collective Communication Benchmark application provides in-depth analysis of:
- Collective completion time (CCT) and its impact on training efficiency.
- Algorithm bandwidth and its role in optimizing data exchanges.
- Rank and collective size dynamics affect system-wide performance.
By benchmarking collective operations, AI engineers can optimize the interplay between model partitioning, hyperparameters, and network topology, ensuring maximum efficiency.
Charting the future of AI infrastructure testing
As AI technology continues to evolve, the journey toward optimized infrastructure is an ongoing expedition. AI infrastructure engineers, much like the great explorers of the past, must rely on precision tools, realistic simulations, and strategic optimizations to navigate the vast, uncharted waters of AI computing. With KAI DC Builder, AI pioneers can confidently set sail, armed with the insights needed to explore, optimize, and expand the frontiers of AI performance. Whether refining network architectures, co-tuning workloads like in Figure 5, or benchmarking collective operations, KAI DC Builder is the ultimate navigator for the modern AI voyage.
Keysight’s AI data center solutions provide end-to-end visibility and optimization across the entire infrastructure—covering compute, interconnect, and network layers—so you can validate performance, uncover bottlenecks, and scale with confidence. To explore how KAI DC Builder emulates real-world AI workloads and provides deep visibility into every data path—from processor to interconnect—check out the full AI data center solution overview. See our latest innovations in AI data center testing at our Keysight KAI Solutions Showcase, featuring expert insights, live Q&A, and breakthrough tools for optimizing performance at scale.