The Android Lens: Applying Mobile Forensics to AI Performance

Modern LLMs like Ollama are technically ground-breaking but suffer from significant thermal and energy inefficiencies on resource-constrained hardware. This is often an overlooked cost of LLM's deterministic nature of token generation along with the heavy, unoptimized CPU operations within the underlying math engines.

With AI data centers, high energy demand translates to high water usage/thermal dissipation needs, and we are seeing it in pushback with American communities. To solve this, we must adopt a more frugal philosophy, the way Android development does.

This talk explores the forensics of loading local AI models to emulate resource constraints. Through the lens of Android and Kotlin CMP, we will audit Ollama's source code and profile how it performs in real time. We will move past the high-level Go wrappers and into the unforgiving C++ threading and memory management, identifying the leaks and bottlenecks leading AI to drown in thermal throttling and hallucinations.