In just the past few years, large machine learning models have made incredible strides. Today’s models are not only remarkably capable but also achieve impressive results across a range of applications, from software engineering and scientific research to content creation and data analysis. With the arrival of models like Kimi-K2.5 and GLM-5, the pace of progress shows no sign of slowing down. (Ki...
The article can be seen as a showcase of the ongoing efforts to optimize AI workloads for practical hardware, cost, and energy constraints. It highlights Dropbox's approach to this optimization, focusing on large-scale models for various services. The challenges in adopting formats such as MXFP and NVFP are acknowledged, suggesting an ongoing need for advancements in software design, framework support, and quantization techniques to preserve model quality at scale.
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