Advancements in AI have enabled the development of tiny models that can run efficiently on devices with limited resources, such as the Raspberry Pi. These models, including Qwen3, Exaone, Ministral, Jamba Reasoning, Granite, and Phi-4 Mini, leverage modern architectures and quantization techniques to deliver high performance in tasks like text generation, vision understanding, and tool usage. Despite their small size, they outperform older, larger models in real-world applications, offering capabilities such as long-context processing, multilingual support, and efficient reasoning. These models demonstrate that compact AI systems can be both powerful and practical for low-power devices, making local AI inference more accessible and cost-effective. This matters because it highlights the potential for deploying advanced AI capabilities on everyday devices, broadening the scope of AI applications without the need for extensive computing infrastructure.
