Side-by-side AI Architecture Comparison Infographic

{"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","Esmple dimesiegn","DeepSeek V3/R1","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":10,"items":["Total parameters: 671B","Active parameters per token: 37B (1 + 8 experts)","Hidden size: 7,128","Esmple dimesiegn: 28,432","Intermediate (FF): 2,048","Attention heads: 128","Context length: 128k","Embedding dimension: First 3 blocks","Context ler length: 22G7","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":10,"items":["Total parameters: 1.2T","Active parameters per token: 52B (1 + 8 experts)","Hidden size: 7,2B","Esmple dimesiegn: 28,432","Intermediate (FF): 3,072","Attention heads: 128","Context length: 256k","Embedding dimension: First 3 blocks","Context ler length: 22G7","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 all V3/R1 emphasis and blue used for all V4 emphasis. Include a small bottom row of compact tabular metrics spanning the width. Preserve the slightly imperfect, human-made infographic look with very small text and crowded annotations."}

• Modelgpt-image-2
• Use casesocial-media-post
• Styleminimal, editorial
• Aspect ratioFlexible

• Use casesocial-media-post
• Styleminimal, editorial
• Recommended specs1:1, 4:5, 16:9, or 9:16
• ModelGPT Image 2

Use this Side-by-side AI Architecture Comparison Infographic prompt as a base. Replace the subject, product details, environment, lighting, brand colors, and output ratio while keeping the social-media-post intent and minimal, editorial direction stable.

• SubjectReplace the product, person, scene, interface, or object being generated.
• Use caseKeep or rewrite the destination as social media post, ad creative, cover image, poster, or ecommerce hero.
• FrameChoose 1:1, 4:5, 16:9, or 9:16 based on the final placement.
• Brand cuesAdd colors, materials, props, text-safe space, and the platform where the image will be used.

Choose the frame for the destination first, then use 1K, 2K, or 4K based on output quality needs. Product images and posters usually benefit from higher resolution.

• CheckSubject, lighting, composition, and frame are specific enough to review.
• CheckText-safe space is included when the image will carry a headline or ad copy.
• CheckResolution, credit cost, and final destination are checked before generation.

Side-by-side AI Architecture Comparison Infographic works because it starts from the image job, then controls subject, composition, lighting, style, and frame. It is best for social media post while keeping the minimal, editorial direction stable.

• Can I use it directly?Yes. Replace the subject, brand cues, and frame, then generate in Image2Studio.
• Which ratio should I use?Choose 1:1, 4:5, 16:9, or 9:16 based on the destination.