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.