LLM Architecture Deep Dive — ChatGLM, Llama, Baichuan Compared
Comparative analysis of major LLM architectures examining attention mechanisms, position encoding, normalization strategies, and scaling approaches.
2026 update note
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Editorial note for 2026. This does not replace the historical article below.
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Recent picks: Observability · Graph RAG · Prompting & tools · Small-model deployment
LLM Architecture Deep Dive — Compared
LLaMA uses RoPE + RMSNorm + SwiGLU + GQA. ChatGLM uses bidirectional prefix attention. Baichuan uses ALiBi encoding with wider layers.
| Feature | LLaMA | ChatGLM | Baichuan |
|---|---|---|---|
| Position | RoPE | RoPE | ALiBi |
| Norm | RMSNorm | RMSNorm | RMSNorm |
| Activation | SwiGLU | GeGLU | SwiGLU |
RoPE enables better length extrapolation. Performance differences come mainly from data quality and training methodology.
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