How to deploy on-device Small Language Models (SLMs) using Apple CoreML and Android NNAPI alongside streaming cloud inference.
1. On-Device vs Cloud Inference Latency
Tradeoffs between 4-bit quantized local models running on NPU versus low-latency SSE streaming APIs.
In modern production environments, ensuring architectural scalability requires rigorous testing and continuous monitoring. Engineering teams must measure latency bounds, memory allocation overhead, and maintain comprehensive regression test suites.
Prioritize deterministic benchmarks and automated profiling tools to detect performance regressions before deployment.
2. Battery Drain & Thermal Throttling
Best practices for offloading token generation to prevent GPU thermal saturation on mobile hardware.
In modern production environments, ensuring architectural scalability requires rigorous testing and continuous monitoring. Engineering teams must measure latency bounds, memory allocation overhead, and maintain comprehensive regression test suites.
Prioritize deterministic benchmarks and automated profiling tools to detect performance regressions before deployment.
3. Offline-First Agentic Workflows
Implementing SQLite vector embeddings and local RAG retrieval directly on user devices.
In modern production environments, ensuring architectural scalability requires rigorous testing and continuous monitoring. Engineering teams must measure latency bounds, memory allocation overhead, and maintain comprehensive regression test suites.
Prioritize deterministic benchmarks and automated profiling tools to detect performance regressions before deployment.
Summary & Next Steps
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