The Big LLM Architecture Comparison @SebastianRaschka
The Big LLM Architecture Comparison  @SebastianRaschka
Uploaded September 2025 | Updated September 2026, 2 weeks ago
Article: magazine.sebastianraschka.com/p/the-big-llm-architecture-comparison
Reasoning from scratch book: https://mng.bz/Nwr7
LLMs from Scratch repo: github.com/rasbt/LLMs-from-scratch

This video covers the most important open-weight LLM architectures released in 2025, along with their architectural design decisions.

00:00:00 The Big Architecture Comparison
00:01:52 1. DeepSeek V3/R1
00:24:50 2. OLMo 2
00:35:07 3. Gemma 3
00:44:31 4. Mistral Small 3.1
00:48:04 5. Llama 4
00:50:03 6. Qwen3
00:58:07 7. SmolLM3
01:05:03 8. Kimi 2
01:08:19 9. GPT-OSS
01:14:57 10. Grok 2.5
01:19:04 11. GLM-4.5
The Big LLM Architecture ComparisonInsights from Finetuning LLMs with Low-Rank AdaptationL14.5 Convolutional Instead of Fully Connected LayersL13.4 Convolutional Filters and Weight-SharingDeep Learning News #10, Apr 3 2021L17.6 A Variational Autoencoder for Face Images in PyTorch   Code ExampleL12.2 Learning Rate Schedulers in PyTorchL5.4 (Optional) Calculus Refresher I: DerivativesL19.2.2 Implementing a Character RNN in PyTorch  Code ExampleHow Claudes Text Watermarking WorksDesigning Generative Adversarial Networks for Privacy-enhanced Face Recognition (Conference rec.)L2.3 The Origins of Deep Learning
Sebastian Raschka |

The Big LLM Architecture Comparison

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