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LLMs Are Complicated Now

ianbarber.blog

June 20, 2026

3 min read

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53/100

Summary

Meta's LLM development has evolved from a straightforward stack of Transformer modules to a more complex architecture. Seb Raschka's gallery allows users to compare model architectures, including Llama 3 and Nem.

Key Takeaways

LLMs have become significantly more complex, incorporating various attention mechanisms and architectures beyond traditional Transformer modules.
Mixture-of-Experts has introduced selective routing in neural networks, enhancing the efficiency of model architectures.
The development of FlexAttention in PyTorch allows for the generation of kernels for a wide range of attention operations while maintaining composability and verifiability.
Andrej Karpathy joined Anthropic to focus on developing richer auto-research loops and emphasizes the importance of composability in model architecture.

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Community Sentiment

Mixed

Positives

The discussion highlights the increasing complexity of LLM architectures, emphasizing the need for deeper engineering efforts to achieve incremental performance gains, which is crucial for advancing AI capabilities.
The mention of various model architectures, like MoE and attention mechanisms, reflects the evolving landscape of LLMs, indicating a rich area for exploration and innovation in AI development.

Concerns

The complexity of modern LLMs, with partial implementations and intricate architectures, poses significant challenges for developers, potentially hindering progress and accessibility in AI applications.
Comparing different families of LLMs without addressing their architectural differences can mislead discussions about performance and capabilities, which is critical for understanding AI advancements.