Network Topology Analysis: Scaling Considerations for Training and Inference
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Brian Martin
Rethink your AI fabric. Discover how rail-based architectures outperform traditional networks, reducing cost and complexity while enabling massive scale for LLMs and MoE models.
Infrastructure Challenges of AI Training and Inference at Scale
The explosive growth of large language models (LLMs) and agentic AI has fundamentally transformed data center networking requirements, pushing traditional architectures beyond their breaking point. Modern AI training workloads, particularly those leveraging the latest NVIDIA GPUs with ever increasing high bandwidth memory, create an insatiable demand for network bandwidth that makes the fabric architecture a key determinant of both system performance and economic viability. The shift from compute-bound to communication-bound workloads has elevated network design to a critical enabler of AI infrastructure success.
Training workloads are characterized by massive, synchronized collective communication patterns, particularly all-reduce and all-gather operations, where hundreds or thousands of GPUs exchange gradient updates and model parameters, creating predictable but enormous bandwidth demands that can overwhelm conventional network fabrics. Inference workloads prioritize low latency and high concurrent throughput for independent request processing, where network delay directly impact user experience and service quality.
The rise of Mixture-of-Experts (MoE) models, especially in conjunction with Agentic AI systems, has additionally altered the network equation, delivering increased token performance while simultaneously reducing collective network communication traffic to levels comparable to much smaller models. These evolutions continue to impact network topology design for both workload types.
