The Big LLM Architecture Comparison

Ahead of AI · 11 months ago

DeepSeek V3 and other recent large language models continue to refine the transformer architecture introduced seven years ago through techniques like Multi-Head Latent Attention for memory efficiency and Mixture-of-Experts for sparse parameter activation, while models like OLMo 2 focus on normalization layer placement and other architectural tweaks. DeepSeek V3 contains 671 billion parameters but uses only 37 billion during inference by activating 9 out of 256 experts per token. These architectural changes allow developers to scale model capacity while maintaining inference efficiency, though the fundamental transformer structure remains largely unchanged from earlier designs.