The Big LLM Architecture Comparison
Ahead of AI Sebastian Raschka, PhD
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.
Why it matters
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
