Local Inference and Layer Splitting

GitHub Profile of Ryan: Mohawk Infrastructure Developer

Transcribed

Local Inference and Layer Splitting

Jun 24, 2026 · 40m 42s

Description

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...

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.

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https://github.com/rwilliamspbg-ops

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