Model Parallelism & Distributed Inference

Key technical highlights covered in this session include: Distributed Layer Splitting: Moving beyond a single device to split large models across multi-node clusters, optimizing the execution path between available CPUs, GPUs, and NPUs. Model Parallelism at Scale: Understanding the strategies used to manage massive LLMs that exceed the memory capacity of a single node, utilizing the engine's high-concurrency session management to maintain throughput. PQC-Secured Edge Offloading: A deep dive into the security protocols that protect private inference data as it is offloaded to edge nodes, ensuring quantum-resistant transport across the distributed cluster. Cluster-Wide Observability: How the management GUI and the Prometheus/Grafana stack track p99 latencies and hardware pressure across the entire distributed inference fleet. High-Concurrency Management: The engineering required to support 100+ concurrent connections in a distributed environment, featuring automatic reconnection and session persistence. Become a supporter of this podcast: https://www.spreaker.com/podcast/deep-dive-of-rwilliamspbg-ops--7123568/support . https://github.com/rwilliamspbg-ops