Hexatronic’s High-Density Racks Enable Efficient AI Training Without Breaking the Power Budget
Artificial Intelligence (AI) workloads are transforming data center operations worldwide. Training AI models requires unprecedented levels of processing power and introduces unique challenges related to hardware, energy consumption, and thermal management.
Traditional CPU-based servers can't keep pace as GPU clusters push data center infrastructure to its limits.
AI training puts intense stress on server racks by packing massive computational horsepower and heat into compact footprints.
As Larry Yang notes in The Fast Mode, “The latest AI server racks cram the heat of 16 gas barbecue grills into the space of a phone booth. To maintain peak efficiency and prevent costly downtime, data center operators must constantly balance energy consumption, cooling capacity, and sustainability goals.”
The solution: Hexatronic's high-density rack and fiber-first infrastructure enable efficient scaling of GPU clusters without exceeding power budgets or overwhelming cooling systems.
By reducing cable bulk, improving airflow, enabling higher fiber counts per rack unit, and supporting 400G/800G interconnects, Hexatronic helps AI facilities build dense, power-efficient compute infrastructure that remains manageable at scale.
Why AI Workloads are Driving a Computing Reset
AI workloads are fundamentally reshaping data center design, from power density and thermal behavior to network bandwidth and cluster topology. Traditional applications simply weren’t built to consume and compute the way AI does.
“AI workloads are associated with tasks related to AI applications, such as generative AI (gen AI), large language models (LLM) like ChatGPT, natural language processing (NLP), and running AI algorithms. AI workloads are differentiated from most other types of workloads by their high levels of complexity and the types of data processed,” explains IBM.
While legacy data center workloads are CPU-centric and relatively tolerant of latency, AI workloads are accelerator-centric, throughput-hungry, and intensely parallel, especially when training large models that operate across hundreds or thousands of GPUs at once.
As Isaac Douglas writes in Digitalisation World, “There’s no doubt that AI is growing ever more impressive, but these capabilities are bringing new challenges in terms of compute power. And, as more industries find novel ways to pack AI into their systems, the pressure on AI’s underpinning infrastructure is reaching an inflection point.”
To illustrate how dramatic the shift truly is, here’s a comparison between traditional data center workloads and today’s AI workloads (especially during training):
| Feature | Traditional Workloads | AI Workloads |
| Processing Pattern | CPU-based with predictable, steady-state loads. | GPU/accelerator dominated with sustained, massively parallel computation. Training requires continuous work across hundreds or thousands of GPUs for days or weeks. |
| Power Consumption | Typically, 5–10 kW per rack, with servers drawing 200–400 W each. | Often 30–100+ kW per rack. Individual GPU servers commonly draw 5–10 kW each. Some liquid-cooled AI racks exceed 100 kW. |
| Heat Density | Moderate and manageable with standard air cooling. | Extremely high and concentrated, 5–10× the heat density of traditional racks, requiring advanced cooling. |
| Network Requirements | Modest bandwidth, often 1G/10G/25G. East–west traffic is present but not overwhelming. | Very high throughput: 400G/800G links increasingly standard. Distributed training requires continuous high-speed GPU-to-GPU communication. Any network bottleneck slows training. |
| Utilization Patterns | Variable or bursty; average utilization often 20–40%. | Near 100% sustained GPU utilization during training runs. These are marathon workloads, not intermittent spikes. |
| Latency Tolerance | Many workloads tolerate milliseconds of latency. | Distributed training is highly latency-sensitive; delays directly degrade model convergence time. |
Hardware Requirements for GPU Clusters
Scaling AI training requires packing more accelerators into each rack, increasing memory bandwidth, boosting interconnected speeds, and deploying network fabrics that behave more like supercomputers than traditional data center networks. These hardware demands push power density, cooling capacity, and rack design to their limits.
Here’s what modern GPU clusters require and why they strain even the most advanced facilities:
- High-Power GPUs: State-of-the-art AI accelerators draw ~400–700 watts per chip, two to three times more power than conventional data center CPUs. When a rack contains dozens of these GPUs, often multiple multi-GPU servers stacked vertically, it’s easy for overall rack power to surge past 50–100 kW. This is one of the primary reasons AI data centers must rethink power distribution and thermal strategies from the ground up.
- High-Bandwidth Memory (HBM): Modern AI accelerators rely on HBM to feed thousands of GPU cores with massive parallel throughput. HBM is fast and power intensive. Large training runs require extreme memory bandwidth, and HBM’s power consumption contributes meaningfully to both rack power density and the cooling burden.
- GPU-to-GPU Interconnects: Training large models means GPUs must constantly exchange gradients, parameters, and activation data. This requires ultra-low-latency, ultra-high-bandwidth communication across the entire GPU cluster.
High-speed interconnects and advanced fabrics create dense mesh topologies that increase both the networking power draw and thermal load.
The denser the fabric, the higher the rack’s power requirement. - High-Storage and I/O: AI training pipelines are notoriously I/O-bound. If storage throughput or PCIe bandwidth cannot keep up, GPUs stall, wasting expensive compute cycles. Effective training requires balanced performance across the entire stack:
- HBM bandwidth
- GPU interconnect bandwidth
- PCIe bus speeds
- Storage throughput
- Network latency
Even a small bottleneck in one layer slows training across the entire cluster.
How Hexatronic’s High-Density Infrastructure Supports Efficient AI Training
Hexatronic’s fiber-first design and high-density racks boost GPU capacity, cut thermal load, and maximize power and network efficiency for AI-driven data centers.
As AI clusters scale toward 50–100 kW racks and 400G/800G fabrics, cutting cable bulk and improving airflow become critical.
Here’s how Hexatronic’s high-density infrastructure gives operators an edge:
- Ultra-High Density Fiber Enclosures: Hexatronic offers fiber enclosures and cassettes that support 96, 144, or even 288 fibers per rack unit, dramatically reducing rack space consumed by patching compared to legacy systems. This frees up vertical space for GPU servers and power distribution hardware.
- Structured Cabling that Preserves Airflow: Hexatronic’s modular structured cabling systems produce clean, predictable layouts that reduce cable congestion and maintain airflow even as port counts grow. Less clutter means colder air reaches the GPUs, lowering cooling overhead and reducing throttling risk.
- Higher GPU Density with Cable Sprawl: Traditional copper-rich racks quickly run out of space or airflow capacity when GPUs scale up. Hexatronic’s fiber-first approach eliminates cable bulk, enabling more GPUs per rack without tangles, obstructions, or reduced serviceability.
- Pre-Terminated Trunk Cables for Fast, Scalable Deployment: Pre-terminated MPO/MTP trunks and high-density panels keep GPU-to-GPU and GPU-to-storage links short, organized, and easy to scale. As operators expand clusters or upgrade to faster fabrics, connections can be added or reconfigured without disruptive rewiring.
- Cooler, Lighter, Low-Heat Cabling: Fiber bundles are thinner, lighter, and generate less heat than equivalent copper cabling. This reduces thermal load inside high-density racks, allowing more of the cooling budget to be dedicated to GPUs rather than compensating for cable-induced heat.
- 400G/800G-Ready for AI Fabrics: Hexatronic’s high-density fiber supports 400G/800G and emerging multi-terabit architectures with low-loss, low-latency performance between servers and GPUs. This is essential for distributed training, where fast collective operations reduce training time and cost.
Hexatronic helps operators scale AI training clusters in dense racks while protecting floor space, preserving airflow, and freeing more of the overall power budget for GPU compute instead of cabling and cooling overhead.
AI is pushing data centers to new limits in power, density, and speed. Hexatronic’s high-density, fiber-first racks give operators the efficiency and scalability needed to keep pace.
Contact Hexatronic today to design a high-density solution built for AI performance.
