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The Big LLM Architecture Comparison

The Big LLM Architecture Comparison

Architectures since GPT-2 still ride transformers. They crank memory and performance with RoPE, swap GQA for MLA, sprinkle in sparse MoE, and roll sliding-window attention. Teams shift RMSNorm. They tweak layer norms with QK-Norm, locking in training stability across modern models.

Trend to watch: In 2025, fine-grained efficiency hacks dethrone sweeping architecture overhauls.


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The FAUN watches over the forest of developers. It roams between Kubernetes clusters, code caves, AI trails, and cloud canopies, gathering the signals that matter and clearing out the noise.
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