5 Key Challenges System Integrators Face When Building Large-Scale AI Clusters

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As organizations race to scale AI, several persistent challenges threaten to slow down projects and increase risk. Below are five common challenges faced by SIs and strategic solutions to address them.

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Scaling AI Clusters Across Multi-Site Deployments

1. Vendor Lock-in and Supply Chain Constriction

The AI industry is heavily influenced by a single vendor that delivers a full-stack proprietary solution from GPUs to networking. While this works for some organizations, it creates vendor lock-in and ties integrators to limited supply cycles. The challenge becomes even greater when demand outpaces availability, leading to costly project delays.

Solution: To mitigate these bottlenecks, integrators should adopt open architectures based on standard Ethernet. This approach allows for vendor flexibility across GPUs, storage systems, switches, NICs, and optics. A vendor-agnostic strategy strengthens the supply chain and minimizes delays caused by reliance on a single supplier.

2. AMD Expertise Gaps

While Nvidia remains the dominant GPU provider, AMD is rapidly gaining market share. Building AMD-based AI infrastructure introduces a new set of challenges. Successful deployment requires expertise in AMD platform bring-up, RCCL tuning, kernel optimizations, and model-level adjustments. It also requires a validated reference architecture that ensures tight integration among compute, networking, and software.

Solution: Many organizations lack these specialized in-house skills. It is crucial for cluster builders to partner with vendors that demonstrate proven AMD experience and offer reference designs that support efficient, large-scale deployments.

3. Insufficient Full-Stack and Cross-Domain Knowledge

Gigascale AI clusters demand skills across many domains, including networking, software orchestration, storage systems, power and cooling engineering, and AI infrastructure operations. Most workload builders do not have this full set of capabilities internally. As clusters grow, the challenge becomes ensuring that all pieces work together as a reliable, scalable, and efficient system.

Solution: This creates the need for vendors who have real experience building complete AI environments and who provide validated architectures that reduce complexity and deployment risk.

4. Performance Optimization at Scale

Studies show that large, distributed AI clusters can lose up to 40% of GPU cycles while waiting for the network to complete collective operations. AI workloads generate constant and rapid communication between xPUs. Even small amounts of congestion slow down the entire training process and increase the overall job completion time (JCT).

Solution: Keeping performance consistent requires careful tuning and ongoing validation across thousands of xPUs. This complexity leads cluster builders to prioritize networking solutions that provide predictable, high-performance operation with minimal or no complex tuning. Simpler performance optimization improves project timelines and reduces operational risk.

5. Insufficient Power and Space Resources

Traditional data center racks draw up to dozens of kW. Modern AI racks often consume over 100 kW, and future systems are expected to require several hundred kW per rack. Existing data centers were not designed for this level of consumption, which creates new constraints on power delivery and local electrical grid capacity.

Solution: A practical solution is to distribute workloads across multiple sites to access more available power and space. This approach, however, creates a connectivity challenge. Integrators must ensure that AI performance is maintained across geographically separated clusters. Doing so requires a networking solution that can scale across distance without introducing packet loss, added latency, or unpredictable performance.

How DriveNets Solves These SI Challenges with a Full-Stack Networking Solution

DriveNets DriveNets provides a complete networking solution specifically designed to address the major challenges facing system integrators:

No vendor lock-in: Our open, Ethernet-based architecture supports any GPU, NIC, or optics vendor, eliminating dependency on a single supplier and dramatically expanding the available procurement pool.

Validated AMD expertise: DriveNets brings deep, hands-on experience in AMD clusters. Through validated reference architectures and a strategic partnership with AMD, we help customers accelerate deployments, reduce operational risk, and maximize AMD workload performance.

End-to-End deployment support: The DriveNets Infrastructure Services (DIS) team supports the full lifecycle of AI and HPC deployments. We provide design, installation, configuration, testing, and performance optimization. Cluster builders can benefit from experts with real deployment experience across hyperscalers, neocloud providers, and large AI-driven enterprises. This on-site team helps improve performance, reduce complexity, and lower deployment risk.

Proven high performance: Our solution delivers a field-proven, high-performance, lossless Ethernet fabric that scales up to 32K GPUs. This performance is delivered out-of-the-box with no complex tuning, which reduces the time-to-first-token (TTFT) and simplifies cluster bring-up.

Long-distance scaling: DriveNets offers a proven multi-site, long-distance, lossless connectivity solution for AI workloads. Our deep buffer switches extend up to 100km per site and connect directly to the backend fabric. With HyperPort, cluster builders can aggregate multiple 800G interfaces into a single 3.2T optical link, allowing you to scale compute across multi-sites and utilize new available power/space while maintaining consistent performance.

Building a large-scale AI cluster is more complex and far more power-intensive than expanding a traditional cloud environment. Make sure you are ready to address these challenges before you begin building a new cluster.

Key Takeaways

Vendor lock-in is now a primary delivery risk, not just a strategic concern.
AMD adoption is accelerating faster than System Integrator expertise.
Large-scale AI clusters break traditional “siloed” infrastructure models.
Network inefficiency is a hidden tax on AI performance at scale.
Power and space constraints are forcing multi-site AI architectures.

Frequently Asked Questions

Why is vendor lock-in a risk when building large-scale AI clusters?

Vendor lock-in limits supply options and can delay deployments when demand exceeds availability. Open, Ethernet-based architectures give system integrators flexibility across GPUs, networking, and optics.

What makes networking critical to AI cluster performance at scale?

AI workloads rely on constant collective communication. Network congestion or packet loss can waste GPU cycles and significantly increase training time, making predictable, lossless networking essential.

How can AI clusters scale when data centers lack sufficient power and space?

Clusters can be distributed across multiple sites to access additional power and capacity, but this requires long-distance, lossless connectivity to maintain consistent AI performance.