How do these next-gen models address efficiency and scalability issues? For example, what are they using instead of attention mechanisms? In your opinion, how sustainable is this approach for future LLMs?
How is DeepSeek's architecture progressing?
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When it comes to efficiency in deep learning architectures, you know how Google's team optimized their transformers, right? I heard that DeepSeek is also investing in "Mixture of Experts" (MoE) approaches, especially in their coding models, by reducing attention mechanisms. I actually worked with MoEs in a project, and by activating only specific "expert" layers in multi-layer models, both memory usage and output speed really improve. In terms of datasets, they're fed directly with coding-focused data, allowing for smarter token focus.
From a future perspective, I think these modular approaches could be a solution against the ever-growing size of LLMs. Back in the day, running large models on a single GPU was impossible, but now, with these layering techniques, they can run efficiently even with limited resources. I once did a project entirely based on transformers, and recently I experimented with an MoE-based model—the results were truly astonishing. I’m fully confident in their sustainability, and efficiency-focused architectures seem to be the trend of the future.