Running small language models (LLMs) on a standard 16GB RAM laptop reveals varying levels of usability, with Qwen 2.5 (14B) offering the best coding performance but consuming significant RAM, leading to crashes when multitasking. Mistral Small (12B) provides a balance between speed and resource demand, though it still causes Windows to swap memory aggressively. Llama-3-8B is more manageable but lacks the reasoning abilities of newer models, while Gemma 3 (9B) excels in instruction following but is resource-intensive. With rising RAM prices, upgrading to 32GB allows for smoother operation without swap lag, presenting a more cost-effective solution than investing in high-end GPUs. This matters because understanding the resource requirements of LLMs can help users optimize their systems without overspending on hardware upgrades.
