# GPT Image Hub prompt

## Infografica Comparativa dell'Architettura dei Modelli
- URL: https://www.gptimagehub.com/it/library/side-by-side-model-architecture-comparison-infographic
- Generator URL: https://www.gptimagehub.com/it/generate?promptId=oss-ym-g2-15614
- Markdown URL: https://www.gptimagehub.com/api/agent/markdown?path=%2Fit%2Flibrary%2Fside-by-side-model-architecture-comparison-infographic
- Category: Infografica / Educazione
- Model: gpt-image-2
- Aspect ratio: 2:1
- Style: graphic-design
- Tags: infografica, diagramma tecnico, architettura, confronto, slide, transformer
- Updated: 2026-10-27T13:35:53.098Z
- Created: 2026-10-27T13:35:53.098Z

Genera un diagramma tecnico per presentazioni che confronta due architetture affiancate. Personalizza i nomi dei modelli e i parametri tra parentesi quadre.

### Full prompt
```text
{"type":"side-by-side AI architecture comparison infographic","style":"clean technical diagram, white background, thin black outlines, rounded rectangles, dashed callout boxes, color-coded highlights, presentation-slide aesthetic, vector infographic","canvas":{"aspect_ratio":"2:1","resolution":"wide horizontal"},"title_row":{"left_title":"[DeepSeek V3/R1 (671 billion)]","right_title":"[DeepSeek V4 (1.2 trillion)]","left_title_color":"bright orange-red","right_title_color":"bright blue"},"layout":{"columns":2,"sections":[{"title":"[DeepSeek V3/R1 (671 billion)]","position":"left half","count":9,"labels":["Vocabulary size of 129k","FeedForward (SwiGLU) module","Intermediate hidden layer dimension of 2,048","MoE layer","Supported context length of 128k tokens","First 3 blocks use dense FFN with hidden size 18,432 instead of MoE","Sample input text","Embedding dimension of 7,168","128 heads"]},{"title":"[DeepSeek V4 (1.2 trillion)]","position":"right half","count":9,"labels":["Vocabulary size of 160k","FeedForward (SwiGLU) module","Intermediate hidden layer dimension of 3,072","MoE layer","Supported context length of 256k tokens","First 3 blocks use dense FFN with hidden size 24,576 instead of MoE","Sample input text","Embedding dimension of 8,192","128 heads"]},{"title":"bottom comparison table","position":"bottom full width","count":10,"labels":["Total parameters","Active parameters per token","Hidden size","Intermediate (FF)","Attention heads","Context length","Embedding dimension","Vocabulary size"]}]},"left_panel":{"background":"very light gray rounded rectangle","main_stack":{"count":8,"blocks":["Tokenized text","Token embedding layer","RMSNorm 1","Multi-head Latent Attention","RMSNorm 2","MoE","Final RMSNorm","Linear output layer"]},"side_module":"RoPE attached to the attention block on the left side","attention_block":{"label":"Multi-head Latent Attention","accent":"orange-red text for the word Latent"},"feedforward_inset":{"title":"FeedForward (SwiGLU) module","count":4,"blocks":["Linear layer","SiLU activation","Linear layer","Linear layer"],"diagram":"two branches multiplied, then projected"},"moe_inset":{"title":"MoE layer","count":5,"blocks":["top combine node","Feed forward","Feed forward","Router","expert count badge 256"],"details":"small black square with 1 selected expert, arrows routing upward to experts, dotted divider line"},"annotations":{"vocab":"Vocabulary size of 129k","ff_dim":"Intermediate hidden layer dimension of 2,048","context":"Supported context length of 128k tokens","dense_first_blocks":"First 3 blocks use dense FFN with hidden size 18,432 instead of MoE","resource_savings":"Resource savings: Model size is 671B but only 1 (shared) + 8 experts active per token; only 37B parameters are active per inference step"},"bottom_stats":{"count":8,"items":["Total parameters: 671B","Active parameters per token: 37B (1 + 8 experts)","Hidden size: 7,168","Intermediate (FF): 2,048","Attention heads: 128","Context length: 128k","Embedding dimension: 7,168","Vocabulary size: 129k"]}},"right_panel":{"background":"very light blue rounded rectangle","main_stack":{"count":8,"blocks":["Tokenized text","Token embedding layer","RMSNorm 1","Multi-head Latent Attention","RMSNorm 2","MoE","Final RMSNorm","Linear output layer"]},"side_module":"RoPE attached to the attention block on the left side","attention_block":{"label":"Multi-head Latent Attention","accent":"blue text for the word Latent"},"feedforward_inset":{"title":"FeedForward (SwiGLU) module","count":4,"blocks":["Linear layer","SiLU activation","Linear layer","Linear layer"],"diagram":"same structure as left panel"},"moe_inset":{"title":"MoE layer","count":5,"blocks":["top combine node","Feed forward","Feed forward","Router","expert count badge 384"],"details":"small black square with 1 selected expert, arrows routing upward to experts, dotted divider line, blue border emphasis"},"annotations":{"vocab":"Vocabulary size of 160k","ff_dim":"Intermediate hidden layer dimension of 3,072","context":"Supported context length of 256k tokens","dense_first_blocks":"First 3 blocks use dense FFN with hidden size 24,576 instead of MoE","resource_savings":"Resource savings: Model size is 1.2T but only 1 (shared) + 8 experts active per token; only 52B parameters are active per inference step"},"bottom_stats":{"count":8,"items":["Total parameters: 1.2T","Active parameters per token: 52B (1 + 8 experts)","Hidden size: 8,192","Intermediate (FF): 3,072","Attention heads: 128","Context length: 256k","Embedding dimension: 8,192","Vocabulary size: 160k"]}},"global_notes":"Create a highly detailed transformer architecture comparison diagram with mirrored layouts. Each half contains one large model stack diagram plus 2 inset diagrams: 1 feedforward module and 1 MoE layer. Use arrows between blocks, tiny technical labels, and connector lines from labels to the relevant components. Keep the typography dense and slide-like, with orange-red used for left-side emphasis and blue used for right-side emphasis. Include a bottom row of compact tabular metrics spanning the width."}
```