Llama-3.2-1B logic fails in mobile meal planning

The 1B parameter class is hitting its reasoning ceiling for multi-turn task planning on mobile devices.

• –Coherence loss in tiny models is often caused by limited attention capacity and KV cache pressure on memory-constrained mobile GPUs.
• –Qwen 2.5 (0.5B and 1.5B) is emerging as a preferred alternative due to superior technical reasoning and multilingual performance in similar footprints.
• –WebLLM and MLC LLM remain the dominant frameworks for bringing these models to mobile browsers via WebGPU.
• –The "tiny model" tradeoff currently forces developers to choose between sub-100ms latency and long-context logical consistency.
• –Developers are increasingly looking toward specialized LoRA adapters to patch logic gaps in models under 2B parameters.