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Distributed System Communication Patterns for AI Infrastructure

7 chapters · 34 lessons

1. 1. Partitioning Strategies for Distributed Inference

   5 lessons

   Learn how to split model computation across devices using tensor, pipeline, and expert parallelism approaches.

2. 2. Collective Communication Primitives

   5 lessons

   Master the core operations that enable multiple devices to share and combine data efficiently.

3. 3. Separation of Concerns: Semantics, Orchestration, and Datapath

   5 lessons

   Design flexible systems by cleanly separating what, when, and how communication happens.

4. 4. Hardware Topology and Network-Aware Optimization

   5 lessons

   Optimize communication patterns based on GPU interconnects, memory hierarchy, and network topology.

5. 5. Request Routing and Batching Strategies

   5 lessons

   Design request handling systems that balance latency, throughput, and resource utilization.

6. 6. Fault Tolerance and Straggler Mitigation

   4 lessons

   Build resilient systems that handle device failures, network issues, and performance variability.

7. 7. Production System Design and Monitoring

   5 lessons

   Architect complete distributed inference systems with observability, scaling, and operational best practices.