The blog discusses the implications of GPUs operating in parallel within AI infrastructure, highlighting the sensitivity to link errors and potential performance reductions.
It emphasizes the importance of focusing on the reliability of optics in AI infrastructure to prevent issues that could lead to significant performance decreases.
The article also mentions Cisco’s past reliability testing of optics from various suppliers, revealing weaknesses even in compliant optics under stressful conditions, urging customers to prioritize reliability in their optical choices.
Title: Understanding the Impact of Parallel GPUs on AI Infrastructure Performance
In the world of AI infrastructure, the use of GPUs operating in parallel has significant implications that differ from traditional network setups. The sensitivity to link errors in parallel GPU setups can lead to performance reductions of up to 40%, requiring workloads to be stopped, backed up to a checkpoint, and restarted. This underscores the critical need for a focus on the reliability of optics within AI infrastructure to maintain seamless operations.
Cisco’s past reliability testing of optics from different suppliers revealed surprising weaknesses, even in optics compliant with industry standards. Stress tests conducted in varying conditions such as temperature, humidity, voltage levels, and signal skew exposed the limitations of seemingly compliant optics. This highlights the importance of selecting optics that can perform reliably under stressful environments to ensure optimal performance in AI infrastructure.
