My wife’s setting up a garden. As a tech fan, I used DFRobot’s Beetle ESP32-C3 (compact, core/expansion boards, many interfaces, lithium support) to make an env monitor (temp, humidity, light, soil moisture) – data on screen & to server via MQTT for future auto irrigation.
In AI's fast evolution, model updates (better performance/efficiency) are common. Llama3.1→3.2 is no different. This article explores their SBC LattePanda Mu x86 benchmarks: speed, efficiency.
This article introduces a method using Intel Optimum-Intel to optimize its performance. The author utilized this method on a LattePanda Sigma, an x86 single board computer/server, leveraging the integrated GPU to accelerate LLaMA 3.1's inference speed, with impressive results.
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