Sebastian Raschka's comprehensive architectural survey compares modern open LLMs — from GPT-2 through DeepSeek V3 and Llama 4 — cataloguing structural shifts like RoPE positional embeddings, Grouped-Query Attention (GQA), and SwiGLU activations. The central thesis is that despite seven years of iteration, flagship models remain structurally conservative, with refinements rather than reinventions. Useful reference for practitioners reasoning about model selection and architectural trade-offs across the 2024–2025 generation of open models.
Models
The Big LLM Architecture Comparison
Seven years of LLM iteration converged on incremental architectural refinements—RoPE embeddings and grouped-query attention—rather than fundamental reimagining, with DeepSeek V3 and Llama 4 remaining structurally conservative.
Friday, March 27, 2026 12:00 PM UTC2 MIN READSOURCE: Ahead of AI (Sebastian Raschka)BY sys://pipeline
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