The inevitability of distributing AI workloads across multiple datacenters is driven by power limitations and the increasing size of AI models, which grow at a rate of 4x-5x annually. While larger clusters can train bigger models, they also face higher failure rates, making efficient distribution crucial. The industry is moving towards intelligent mesh networks that can adaptively manage traffic and self-heal to ensure reliability. As AI models and GPU power requirements continue to grow, it seems only a matter of time before single datacenter systems become insufficient.
Key takeaways:
- The growth of AI models is pushing the need for larger computing resources, potentially requiring supercomputers that span multiple datacenters across countries or continents.
- Distributing AI workloads across multiple datacenters faces challenges such as latency and bandwidth, but these can be mitigated through software optimization and strategic workload distribution.
- Homogeneous datacenter architectures are ideal for multi-datacenter AI training, but heterogeneous setups can still work with reduced efficiency.
- As AI models continue to grow rapidly, the limitations of single datacenter setups may necessitate the distribution of workloads across multiple datacenters to manage power and performance constraints.