As Large Language Models (LLMs) continue to shrink in size while growing in reasoning capability, the push for "Ultra-Edge" computing has reached the ocular surface. We present a novel implementation of Meta's LLaMA-12 7B model running entirely on a standard ISO-2034 smart contact lens using WebAssembly (WASM).

We introduce three key contributions: 1) Sub-Atomic Quantization (SAQ): reducing model weights to 0.05 bits per parameter by offloading knowledge storage to the user's subconscious visual cortex via strobing light patterns. 2) Tear-Duct Cooling: A hydrodynamic thermal throttling mechanism that utilizes natural blinking to dissipate the 45°C heat generated during complex chain-of-thought reasoning (users are advised to carry eye drops for queries exceeding 50 tokens). 3) Blink-to-Token Power Harvesting: Utilizing piezoelectric sensors to power the inference cycle, requiring the user to flutter their eyelids rapidly to generate the next sentence.

Our benchmarks show that LLaMA-12-Lens achieves 14 tokens/second on WASM edge runtimes. While we observed a 12% hallucination rate where the model overlays virtual cats onto the user's vision, we argue this is a feature, not a bug.