Networking is the New Bottleneck for Mixture-of-Experts AI Workloads

While Mixture-of-Experts (MoE) architectures have drastically reduced compute costs, they have exposed a critical networking bottleneck that GPU investment alone cannot fix.

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Unlike the predictable, choreographed communication of dense models, MoE creates “improvisational” and unpredictable traffic patterns that often lead to significant GPU underutilization.

This white paper explores how industry leaders like DeepSeek AI and Meta are already reporting that communication latency can account for up to 50% of training time or 30% of serving latency.

  • Is your network the hidden ceiling for your AI performance?​
  • How do you unlock the full potential of your AI infrastructure?​
  • Shift the focus from raw processing power to optimizing the network fabric that connects it all.

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DriveNets Advances Open Backend Networking for AI with AMD

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