Local Inference and Layer Splitting

Key technical highlights covered in this session include: The Mohawk Inference Engine Architecture: An overview of this lightweight, secure engine engineered for multi-device layer splitting and PQC-secured edge offloading. Multi-Device Layer Splitting: A deep dive into how the engine optimizes model execution by distributing layers across different hardware backends (CPU, GPU, NPU) to maximize throughput and minimize latency. PQC-Secured Edge Offloading: Understanding the security protocols used when offloading inference tasks to edge nodes, ensuring that the transport remains quantum-resistant. Production-Optimized Performance: An analysis of the engine’s scalability features, such as connection pooling for 100+ concurrent connections, and its ability to maintain UI responsiveness through blocking detection. Real-Time Inference Observability: Exploring the specialized GUI that provides real-time tracking of p50/p95/p99 latencies, GPU utilization, and memory pressure for active inference sessions. Enterprise-Grade Hardening: A breakdown of the security defaults, including JWT authentication with RSA signatures, mTLS for worker communication, and Fernet-encrypted configuration storage. Become a supporter of this podcast: https://www.spreaker.com/podcast/deep-dive-of-rwilliamspbg-ops--7123568/support . https://github.com/rwilliamspbg-ops