LLMs take in text, break it into tokens, turn those tokens into embeddings, pass them through layers of transformers, and generate text back out. Models like ChatGPT, Claude, Gemini, Llama, and others all follow this basic process.<\/p>\n
At their core, LLMs are deep learning models trained on massive amounts of text data. This training allows them to understand language, generate responses, summarize information, write code, answer questions, and perform a wide range of tasks. They use the transformer architecture, which is extremely good at handling long sequences and capturing complex patterns in language.<\/p>\n
Today, LLMs are widely accessible through consumer tools and assistants\u2014from OpenAI\u2019s ChatGPT and Anthropic\u2019s Claude to Meta\u2019s Llama models, Microsoft Copilot, and Google\u2019s Gemini and BERT\/PaLM family. They\u2019ve become the foundation of modern AI applications because of their versatility and ease of use.<\/p>\n
![\"\"](\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/12\/image-20.png\")
<\/figure>\n<\/div>\nVision-Language Models (VLMs)<\/strong><\/h1>\nVLMs combine two worlds:<\/strong><\/p>\n\n- A vision encoder that processes images or video<\/li>\n
- A text encoder that processes language<\/li>\n<\/ul>\n
Both streams meet in a multimodal processor, and a language model generates the final output.<\/p>\n
Examples include GPT-4V, Gemini Pro Vision, and LLaVA.<\/p>\n
A VLM is essentially a large language model that has been given the ability to see. By fusing visual and text representations, these models can understand images, interpret documents, answer questions about pictures, describe videos, and more.<\/p>\n
Traditional computer vision models are trained for one narrow task\u2014like classifying cats vs. dogs or extracting text from an image\u2014and they can\u2019t generalize beyond their training classes. If you need a new class or task, you must retrain them from scratch.<\/p>\n
VLMs remove this limitation. Trained on huge datasets of images, videos, and text, they can perform many vision tasks zero-shot, simply by following natural language instructions. They can do everything from image captioning and OCR to visual reasoning and multi-step document understanding\u2014all without task-specific retraining.<\/p>\n
This flexibility makes VLMs one of the most powerful advances in modern AI.<\/p>\n
\n![\"\"](\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/12\/image-18.png\")
<\/figure>\n<\/div>\nMixture of Experts (MoE)<\/strong><\/h1>\nMixture of Experts models build on the standard transformer architecture but introduce a key upgrade: instead of one feed-forward network per layer, they use many smaller expert networks and activate only a few for each token. This makes MoE models extremely efficient while offering massive capacity.<\/p>\n
In a regular transformer, every token flows through the same feed-forward network, meaning all parameters are used for every token. MoE layers replace this with a pool of experts, and a router decides which experts should process each token (Top-K selection). As a result, MoE models may have far more total parameters, but they only compute with a small fraction of them at a time\u2014giving sparse compute.<\/p>\n
For example, Mixtral 8\u00d77B has 46B+ parameters, yet each token uses only about 13B.<\/p>\n
This design drastically reduces inference cost. Instead of scaling by making the model deeper or wider (which increases FLOPs), MoE models scale by adding more experts, boosting capacity without raising per-token compute. This is why MoEs are often described as having \u201cbigger brains at lower runtime cost.\u201d<\/p>\n
\n![\"\"](\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/12\/image-17.png\")
<\/figure>\n<\/div>\nLarge Action Models (LAMs)<\/strong><\/h1>\nLarge Action Models go a step beyond generating text\u2014they turn intent into action. Instead of just answering questions, a LAM can understand what a user wants, break the task into steps, plan the required actions, and then execute them in the real world or on a computer.<\/p>\n
A typical LAM pipeline includes:<\/strong><\/p>\n\n- Perception \u2013 Understanding the user\u2019s input<\/li>\n
- Intent recognition \u2013 Identifying what the user is trying to achieve<\/li>\n
- Task decomposition \u2013 Breaking the goal into actionable steps<\/li>\n
- Action planning + memory \u2013 Choosing the right sequence of actions using past and present context<\/li>\n
- Execution \u2013 Carrying out tasks autonomously<\/li>\n<\/ul>\n
Examples include Rabbit R1, Microsoft\u2019s UFO framework, and Claude Computer Use, all of which can operate apps, navigate interfaces, or complete tasks on behalf of a user.<\/p>\n
LAMs are trained on massive datasets of real user actions, giving them the ability to not just respond, but act\u2014booking rooms, filling forms, organizing files, or performing multi-step workflows. This shifts AI from a passive assistant into an active agent capable of complex, real-time decision-making.<\/p>\n![\"\"](\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/12\/image-16-1024x464.png\")
<\/figure>\nSmall Language Models (SLMs)<\/strong><\/h1>\nSLMs are lightweight language models designed to run efficiently on edge devices, mobile hardware, and other resource-constrained environments. They use compact tokenization, optimized transformer layers, and aggressive quantization to make local, on-device deployment possible. Examples include Phi-3, Gemma, Mistral 7B, and Llama 3.2 1B.<\/p>\n
Unlike LLMs, which may have hundreds of billions of parameters, SLMs typically range from a few million to a few billion. Despite their smaller size, they can still understand and generate natural language, making them useful for chat, summarization, translation, and task automation\u2014without needing cloud computation.<\/p>\n
Because they require far less memory and compute, SLMs are ideal for:<\/strong><\/p>\n\n- Mobile apps<\/li>\n
- IoT and edge devices<\/li>\n
- Offline or privacy-sensitive scenarios<\/li>\n
- Low-latency applications where cloud calls are too slow<\/li>\n<\/ul>\n
SLMs represent a growing shift toward fast, private, and cost-efficient AI, bringing language intelligence directly onto personal devices.<\/p>\n
\n![\"\"](\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/12\/image-15.png\")
<\/figure>\n<\/div>\nThe post 5 AI Model Architectures Every AI Engineer Should Know<\/a> appeared first on MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"Everyone talks about LLMs\u2014but today\u2019s AI ecosystem is far bigger than just language models. Behind the scenes, a whole family of specialized architectures is quietly transforming how machines see, plan, act, segment, represent concepts, and even run efficiently on small devices. Each of these models solves a different part of the intelligence puzzle, and together they\u2019re shaping the next generation of AI systems. In this article, we\u2019ll explore the five major players: Large Language Models (LLMs), Vision-Language Models (VLMs), Mixture of Experts (MoE), Large Action Models (LAMs) & Small Language Models (SLMs). Large Language Models (LLMs) LLMs take in text, break it into tokens, turn those tokens into embeddings, pass them through layers of transformers, and generate text back out. Models like ChatGPT, Claude, Gemini, Llama, and others all follow this basic process. At their core, LLMs are deep learning models trained on massive amounts of text data. This training allows them to understand language, generate responses, summarize information, write code, answer questions, and perform a wide range of tasks. They use the transformer architecture, which is extremely good at handling long sequences and capturing complex patterns in language. Today, LLMs are widely accessible through consumer tools and assistants\u2014from OpenAI\u2019s ChatGPT and Anthropic\u2019s Claude to Meta\u2019s Llama models, Microsoft Copilot, and Google\u2019s Gemini and BERT\/PaLM family. They\u2019ve become the foundation of modern AI applications because of their versatility and ease of use. Vision-Language Models (VLMs) VLMs combine two worlds: A vision encoder that processes images or video A text encoder that processes language Both streams meet in a multimodal processor, and a language model generates the final output. Examples include GPT-4V, Gemini Pro Vision, and LLaVA. A VLM is essentially a large language model that has been given the ability to see. By fusing visual and text representations, these models can understand images, interpret documents, answer questions about pictures, describe videos, and more. Traditional computer vision models are trained for one narrow task\u2014like classifying cats vs. dogs or extracting text from an image\u2014and they can\u2019t generalize beyond their training classes. If you need a new class or task, you must retrain them from scratch. VLMs remove this limitation. Trained on huge datasets of images, videos, and text, they can perform many vision tasks zero-shot, simply by following natural language instructions. They can do everything from image captioning and OCR to visual reasoning and multi-step document understanding\u2014all without task-specific retraining. This flexibility makes VLMs one of the most powerful advances in modern AI. Mixture of Experts (MoE) Mixture of Experts models build on the standard transformer architecture but introduce a key upgrade: instead of one feed-forward network per layer, they use many smaller expert networks and activate only a few for each token. This makes MoE models extremely efficient while offering massive capacity. In a regular transformer, every token flows through the same feed-forward network, meaning all parameters are used for every token. MoE layers replace this with a pool of experts, and a router decides which experts should process each token (Top-K selection). As a result, MoE models may have far more total parameters, but they only compute with a small fraction of them at a time\u2014giving sparse compute. For example, Mixtral 8\u00d77B has 46B+ parameters, yet each token uses only about 13B. This design drastically reduces inference cost. Instead of scaling by making the model deeper or wider (which increases FLOPs), MoE models scale by adding more experts, boosting capacity without raising per-token compute. This is why MoEs are often described as having \u201cbigger brains at lower runtime cost.\u201d Large Action Models (LAMs) Large Action Models go a step beyond generating text\u2014they turn intent into action. Instead of just answering questions, a LAM can understand what a user wants, break the task into steps, plan the required actions, and then execute them in the real world or on a computer. A typical LAM pipeline includes: Perception \u2013 Understanding the user\u2019s input Intent recognition \u2013 Identifying what the user is trying to achieve Task decomposition \u2013 Breaking the goal into actionable steps Action planning + memory \u2013 Choosing the right sequence of actions using past and present context Execution \u2013 Carrying out tasks autonomously Examples include Rabbit R1, Microsoft\u2019s UFO framework, and Claude Computer Use, all of which can operate apps, navigate interfaces, or complete tasks on behalf of a user. LAMs are trained on massive datasets of real user actions, giving them the ability to not just respond, but act\u2014booking rooms, filling forms, organizing files, or performing multi-step workflows. This shifts AI from a passive assistant into an active agent capable of complex, real-time decision-making. Small Language Models (SLMs) SLMs are lightweight language models designed to run efficiently on edge devices, mobile hardware, and other resource-constrained environments. They use compact tokenization, optimized transformer layers, and aggressive quantization to make local, on-device deployment possible. Examples include Phi-3, Gemma, Mistral 7B, and Llama 3.2 1B. Unlike LLMs, which may have hundreds of billions of parameters, SLMs typically range from a few million to a few billion. Despite their smaller size, they can still understand and generate natural language, making them useful for chat, summarization, translation, and task automation\u2014without needing cloud computation. Because they require far less memory and compute, SLMs are ideal for: Mobile apps IoT and edge devices Offline or privacy-sensitive scenarios Low-latency applications where cloud calls are too slow SLMs represent a growing shift toward fast, private, and cost-efficient AI, bringing language intelligence directly onto personal devices. The post 5 AI Model Architectures Every AI Engineer Should Know appeared first on MarkTechPost.<\/p>","protected":false},"author":2,"featured_media":57172,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-57171","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\n
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NU","author_link":"https:\/\/youzum.net\/it\/members\/adminnu\/"},"rttpg_comment":0,"rttpg_category":"<a href=\"https:\/\/youzum.net\/it\/category\/ai-club\/\" rel=\"category tag\">AI<\/a> <a href=\"https:\/\/youzum.net\/it\/category\/committee\/\" rel=\"category tag\">Committee<\/a> <a href=\"https:\/\/youzum.net\/it\/category\/news\/\" rel=\"category tag\">News<\/a> <a href=\"https:\/\/youzum.net\/it\/category\/uncategorized\/\" rel=\"category tag\">Uncategorized<\/a>","rttpg_excerpt":"Everyone talks about LLMs\u2014but today\u2019s AI ecosystem is far bigger than just language models. Behind the scenes, a whole family of specialized architectures is quietly transforming how machines see, plan, act, segment, represent concepts, and even run efficiently on small devices. Each of these models solves a different part of the intelligence puzzle, and together…","_links":{"self":[{"href":"https:\/\/youzum.net\/it\/wp-json\/wp\/v2\/posts\/57171","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/youzum.net\/it\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/youzum.net\/it\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/youzum.net\/it\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/youzum.net\/it\/wp-json\/wp\/v2\/comments?post=57171"}],"version-history":[{"count":0,"href":"https:\/\/youzum.net\/it\/wp-json\/wp\/v2\/posts\/57171\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/youzum.net\/it\/wp-json\/wp\/v2\/media\/57172"}],"wp:attachment":[{"href":"https:\/\/youzum.net\/it\/wp-json\/wp\/v2\/media?parent=57171"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/youzum.net\/it\/wp-json\/wp\/v2\/categories?post=57171"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/youzum.net\/it\/wp-json\/wp\/v2\/tags?post=57171"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- \n
- A vision encoder that processes images or video<\/li>\n
- A text encoder that processes language<\/li>\n<\/ul>\n
Both streams meet in a multimodal processor, and a language model generates the final output.<\/p>\n
Examples include GPT-4V, Gemini Pro Vision, and LLaVA.<\/p>\n
A VLM is essentially a large language model that has been given the ability to see. By fusing visual and text representations, these models can understand images, interpret documents, answer questions about pictures, describe videos, and more.<\/p>\n
Traditional computer vision models are trained for one narrow task\u2014like classifying cats vs. dogs or extracting text from an image\u2014and they can\u2019t generalize beyond their training classes. If you need a new class or task, you must retrain them from scratch.<\/p>\n
VLMs remove this limitation. Trained on huge datasets of images, videos, and text, they can perform many vision tasks zero-shot, simply by following natural language instructions. They can do everything from image captioning and OCR to visual reasoning and multi-step document understanding\u2014all without task-specific retraining.<\/p>\n
This flexibility makes VLMs one of the most powerful advances in modern AI.<\/p>\n
\n<\/figure>\n<\/div>\nMixture of Experts (MoE)<\/strong><\/h1>\n
Mixture of Experts models build on the standard transformer architecture but introduce a key upgrade: instead of one feed-forward network per layer, they use many smaller expert networks and activate only a few for each token. This makes MoE models extremely efficient while offering massive capacity.<\/p>\n
In a regular transformer, every token flows through the same feed-forward network, meaning all parameters are used for every token. MoE layers replace this with a pool of experts, and a router decides which experts should process each token (Top-K selection). As a result, MoE models may have far more total parameters, but they only compute with a small fraction of them at a time\u2014giving sparse compute.<\/p>\n
For example, Mixtral 8\u00d77B has 46B+ parameters, yet each token uses only about 13B.<\/p>\n
This design drastically reduces inference cost. Instead of scaling by making the model deeper or wider (which increases FLOPs), MoE models scale by adding more experts, boosting capacity without raising per-token compute. This is why MoEs are often described as having \u201cbigger brains at lower runtime cost.\u201d<\/p>\n
\n<\/figure>\n<\/div>\nLarge Action Models (LAMs)<\/strong><\/h1>\n
Large Action Models go a step beyond generating text\u2014they turn intent into action. Instead of just answering questions, a LAM can understand what a user wants, break the task into steps, plan the required actions, and then execute them in the real world or on a computer.<\/p>\n
A typical LAM pipeline includes:<\/strong><\/p>\n
- \n
- Perception \u2013 Understanding the user\u2019s input<\/li>\n
- Intent recognition \u2013 Identifying what the user is trying to achieve<\/li>\n
- Task decomposition \u2013 Breaking the goal into actionable steps<\/li>\n
- Action planning + memory \u2013 Choosing the right sequence of actions using past and present context<\/li>\n
- Execution \u2013 Carrying out tasks autonomously<\/li>\n<\/ul>\n
Examples include Rabbit R1, Microsoft\u2019s UFO framework, and Claude Computer Use, all of which can operate apps, navigate interfaces, or complete tasks on behalf of a user.<\/p>\n
LAMs are trained on massive datasets of real user actions, giving them the ability to not just respond, but act\u2014booking rooms, filling forms, organizing files, or performing multi-step workflows. This shifts AI from a passive assistant into an active agent capable of complex, real-time decision-making.<\/p>\n
<\/figure>\nSmall Language Models (SLMs)<\/strong><\/h1>\n
SLMs are lightweight language models designed to run efficiently on edge devices, mobile hardware, and other resource-constrained environments. They use compact tokenization, optimized transformer layers, and aggressive quantization to make local, on-device deployment possible. Examples include Phi-3, Gemma, Mistral 7B, and Llama 3.2 1B.<\/p>\n
Unlike LLMs, which may have hundreds of billions of parameters, SLMs typically range from a few million to a few billion. Despite their smaller size, they can still understand and generate natural language, making them useful for chat, summarization, translation, and task automation\u2014without needing cloud computation.<\/p>\n
Because they require far less memory and compute, SLMs are ideal for:<\/strong><\/p>\n
- \n
- Mobile apps<\/li>\n
- IoT and edge devices<\/li>\n
- Offline or privacy-sensitive scenarios<\/li>\n
- Low-latency applications where cloud calls are too slow<\/li>\n<\/ul>\n
SLMs represent a growing shift toward fast, private, and cost-efficient AI, bringing language intelligence directly onto personal devices.<\/p>\n
\n<\/figure>\n<\/div>\nThe post 5 AI Model Architectures Every AI Engineer Should Know<\/a> appeared first on MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"
Everyone talks about LLMs\u2014but today\u2019s AI ecosystem is far bigger than just language models. Behind the scenes, a whole family of specialized architectures is quietly transforming how machines see, plan, act, segment, represent concepts, and even run efficiently on small devices. Each of these models solves a different part of the intelligence puzzle, and together they\u2019re shaping the next generation of AI systems. In this article, we\u2019ll explore the five major players: Large Language Models (LLMs), Vision-Language Models (VLMs), Mixture of Experts (MoE), Large Action Models (LAMs) & Small Language Models (SLMs). Large Language Models (LLMs) LLMs take in text, break it into tokens, turn those tokens into embeddings, pass them through layers of transformers, and generate text back out. Models like ChatGPT, Claude, Gemini, Llama, and others all follow this basic process. At their core, LLMs are deep learning models trained on massive amounts of text data. This training allows them to understand language, generate responses, summarize information, write code, answer questions, and perform a wide range of tasks. They use the transformer architecture, which is extremely good at handling long sequences and capturing complex patterns in language. Today, LLMs are widely accessible through consumer tools and assistants\u2014from OpenAI\u2019s ChatGPT and Anthropic\u2019s Claude to Meta\u2019s Llama models, Microsoft Copilot, and Google\u2019s Gemini and BERT\/PaLM family. They\u2019ve become the foundation of modern AI applications because of their versatility and ease of use. Vision-Language Models (VLMs) VLMs combine two worlds: A vision encoder that processes images or video A text encoder that processes language Both streams meet in a multimodal processor, and a language model generates the final output. Examples include GPT-4V, Gemini Pro Vision, and LLaVA. A VLM is essentially a large language model that has been given the ability to see. By fusing visual and text representations, these models can understand images, interpret documents, answer questions about pictures, describe videos, and more. Traditional computer vision models are trained for one narrow task\u2014like classifying cats vs. dogs or extracting text from an image\u2014and they can\u2019t generalize beyond their training classes. If you need a new class or task, you must retrain them from scratch. VLMs remove this limitation. Trained on huge datasets of images, videos, and text, they can perform many vision tasks zero-shot, simply by following natural language instructions. They can do everything from image captioning and OCR to visual reasoning and multi-step document understanding\u2014all without task-specific retraining. This flexibility makes VLMs one of the most powerful advances in modern AI. Mixture of Experts (MoE) Mixture of Experts models build on the standard transformer architecture but introduce a key upgrade: instead of one feed-forward network per layer, they use many smaller expert networks and activate only a few for each token. This makes MoE models extremely efficient while offering massive capacity. In a regular transformer, every token flows through the same feed-forward network, meaning all parameters are used for every token. MoE layers replace this with a pool of experts, and a router decides which experts should process each token (Top-K selection). As a result, MoE models may have far more total parameters, but they only compute with a small fraction of them at a time\u2014giving sparse compute. For example, Mixtral 8\u00d77B has 46B+ parameters, yet each token uses only about 13B. This design drastically reduces inference cost. Instead of scaling by making the model deeper or wider (which increases FLOPs), MoE models scale by adding more experts, boosting capacity without raising per-token compute. This is why MoEs are often described as having \u201cbigger brains at lower runtime cost.\u201d Large Action Models (LAMs) Large Action Models go a step beyond generating text\u2014they turn intent into action. Instead of just answering questions, a LAM can understand what a user wants, break the task into steps, plan the required actions, and then execute them in the real world or on a computer. A typical LAM pipeline includes: Perception \u2013 Understanding the user\u2019s input Intent recognition \u2013 Identifying what the user is trying to achieve Task decomposition \u2013 Breaking the goal into actionable steps Action planning + memory \u2013 Choosing the right sequence of actions using past and present context Execution \u2013 Carrying out tasks autonomously Examples include Rabbit R1, Microsoft\u2019s UFO framework, and Claude Computer Use, all of which can operate apps, navigate interfaces, or complete tasks on behalf of a user. LAMs are trained on massive datasets of real user actions, giving them the ability to not just respond, but act\u2014booking rooms, filling forms, organizing files, or performing multi-step workflows. This shifts AI from a passive assistant into an active agent capable of complex, real-time decision-making. Small Language Models (SLMs) SLMs are lightweight language models designed to run efficiently on edge devices, mobile hardware, and other resource-constrained environments. They use compact tokenization, optimized transformer layers, and aggressive quantization to make local, on-device deployment possible. Examples include Phi-3, Gemma, Mistral 7B, and Llama 3.2 1B. Unlike LLMs, which may have hundreds of billions of parameters, SLMs typically range from a few million to a few billion. Despite their smaller size, they can still understand and generate natural language, making them useful for chat, summarization, translation, and task automation\u2014without needing cloud computation. Because they require far less memory and compute, SLMs are ideal for: Mobile apps IoT and edge devices Offline or privacy-sensitive scenarios Low-latency applications where cloud calls are too slow SLMs represent a growing shift toward fast, private, and cost-efficient AI, bringing language intelligence directly onto personal devices. The post 5 AI Model Architectures Every AI Engineer Should Know appeared first on MarkTechPost.<\/p>","protected":false},"author":2,"featured_media":57172,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-57171","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\n
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