[pama]

Not sure what you are referring to—do you have a pointer to a technical writeup perhaps? In training and inference MLA has way less flops than MHA, which is the gold standard, and way better accuracy (model performance) than GQA (see comparisons in the DeepSeek papers or try deepseek models vs llama for long context.)

More generally, with any hardware architecture you use, you can optimize the throughput for your main goal (initially training; later inference) by balancing other parameters of the architecture. Even if training is suboptimal, if you want to make a global impact with a public model, you aim for the next NVidia inference hardware.