Model Parallelism & Distributed Inference

Transcribed

Model Parallelism & Distributed Inference

Jun 24, 2026 · 37m 32s

Description

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...

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.

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