@_avichawla: 8 AI model architectures, visually explained: There's a tendency to treat LLMs as the whole field. But they're one fami…

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Summary

A visual breakdown of 8 major AI model architectures including LLMs, VLMs, MoE, SLMs, and more, plus a bonus mention of recursive language models from MIT.

8 AI model architectures, visually explained: There's a tendency to treat LLMs as the whole field. But they're one family among several others, each shaped by a different kind of input, output, or constraint. Here's a breakdown: 1. LLM (Large language models) Text goes in, gets tokenized into embeddings, processed through transformers, and text comes out. ↳ GPT, Claude, Gemini, Llama. 2. LCM (Large concept models) Works at the concept level, not tokens. Input is segmented into sentences, passed through SONAR embeddings, and then uses diffusion before output. ↳ Meta's LCM is the pioneer. 3. LAM (Large action models) Turns intent into action. Input flows through perception, intent recognition, task breakdown, then action planning with memory before executing. ↳ Rabbit R1, Microsoft UFO, Claude Computer Use. 4. MoE (Mixture of experts) A router decides which specialized "experts" handle your query. Only relevant experts activate. Results go through selection and processing. ↳ Mixtral, GPT-4, DeepSeek. 5. VLM (Vision-language models) Images pass through a vision encoder, text through a text encoder. Both fuse in a multimodal processor, then a language model generates output. ↳ GPT-4V, Gemini Pro Vision, LLaVA. 6. SLM (Small language models) LLMs optimized for edge devices using compact tokenization, efficient transformers, and quantization for local deployment. ↳ Phi-3, Gemma, Mistral 7B, Llama 3.2 1B. 7. MLM (Masked language models) Tokens get masked, converted to embeddings, then processed bidirectionally to predict hidden words. ↳ BERT, RoBERTa, DeBERTa power search and sentiment analysis. 8. SAM (Segment anything models) Prompts and images go through separate encoders, feed into a mask decoder to produce pixel-perfect segmentation. ↳ Meta's SAM powers photo editing, medical imaging, and autonomous vehicles. That said, the 8 architectures above are the established families. There's one I left out of the image on purpose, because it's less a model family and more a frontier idea called recursive language models from MIT. Rather than fighting context rot with a bigger window, an RLM stores your prompt as a variable in a Python environment and lets a root model write code to inspect it, then spins up recursive sub-calls to process the relevant chunks. The model reasons over the context instead of drowning in it, which lets it scale to 10M+ tokens without retraining and beats the base model even on inputs that already fit. I wrote a full breakdown on it recently. Read it below.
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8 AI model architectures, visually explained:

There’s a tendency to treat LLMs as the whole field. But they’re one family among several others, each shaped by a different kind of input, output, or constraint.

Here’s a breakdown:

  1. LLM (Large language models)

Text goes in, gets tokenized into embeddings, processed through transformers, and text comes out.

↳ GPT, Claude, Gemini, Llama.

  1. LCM (Large concept models)

Works at the concept level, not tokens. Input is segmented into sentences, passed through SONAR embeddings, and then uses diffusion before output.

↳ Meta’s LCM is the pioneer.

  1. LAM (Large action models)

Turns intent into action. Input flows through perception, intent recognition, task breakdown, then action planning with memory before executing.

↳ Rabbit R1, Microsoft UFO, Claude Computer Use.

  1. MoE (Mixture of experts)

A router decides which specialized “experts” handle your query. Only relevant experts activate. Results go through selection and processing.

↳ Mixtral, GPT-4, DeepSeek.

  1. VLM (Vision-language models)

Images pass through a vision encoder, text through a text encoder. Both fuse in a multimodal processor, then a language model generates output.

↳ GPT-4V, Gemini Pro Vision, LLaVA.

  1. SLM (Small language models)

LLMs optimized for edge devices using compact tokenization, efficient transformers, and quantization for local deployment.

↳ Phi-3, Gemma, Mistral 7B, Llama 3.2 1B.

  1. MLM (Masked language models)

Tokens get masked, converted to embeddings, then processed bidirectionally to predict hidden words.

↳ BERT, RoBERTa, DeBERTa power search and sentiment analysis.

  1. SAM (Segment anything models)

Prompts and images go through separate encoders, feed into a mask decoder to produce pixel-perfect segmentation.

↳ Meta’s SAM powers photo editing, medical imaging, and autonomous vehicles.

That said, the 8 architectures above are the established families.

There’s one I left out of the image on purpose, because it’s less a model family and more a frontier idea called recursive language models from MIT.

Rather than fighting context rot with a bigger window, an RLM stores your prompt as a variable in a Python environment and lets a root model write code to inspect it, then spins up recursive sub-calls to process the relevant chunks.

The model reasons over the context instead of drowning in it, which lets it scale to 10M+ tokens without retraining and beats the base model even on inputs that already fit.

I wrote a full breakdown on it recently.

Read it below.

happy to help.

good point. Since you mentioned MoE, I also have this visual I created that shows how they work:

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