{"id":3493,"date":"2026-08-07T11:41:09","date_gmt":"2026-08-07T11:41:09","guid":{"rendered":"https:\/\/i10xnews.kinsta.cloud\/lfm2-5-2-6b-liquid-ai-edge-model"},"modified":"2026-08-07T11:41:09","modified_gmt":"2026-08-07T11:41:09","slug":"lfm2-5-2-6b-liquid-ai-edge-model","status":"publish","type":"post","link":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model","title":{"rendered":"LFM2.5-2.6B: Liquid AI&#8217;s On-Device Agent Model"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Summary<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>LFM2.5-2.6B<\/strong> is a highly compact, 2.6-billion-parameter model from Liquid AI that can run agentic workflows entirely on-device, including on hardware as minimal as a Raspberry Pi.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What happened<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Bypassing the need for cloud APIs or dedicated GPUs, Liquid AI released an edge-native model designed specifically for tool-use, function-calling, and autonomous tasks in highly constrained compute environments.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why it matters now<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">While the AI industry obsesses over massive, energy-hungry gigawatt data centers, Liquid AI is proving that localized, zero-latency, privacy-first agents can operate at the absolute edge, directly challenging the traditional cloud-compute toll bridge.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Who is most affected<\/h2>\n\n\n<ul class=\"wp-block-list\"><li>Edge and embedded developers<\/li><li>Enterprise CTOs seeking private AI workflows<\/li><li>Hardware makers in the IoT space<\/li><li>Cloud infrastructure providers facing potential inference workload erosion<\/li><\/ul>\n\n\n<h2 class=\"wp-block-heading\">The under-reported angle<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Mainstream coverage treats this as a novelty for hobbyists, but the real story is the underlying architecture shift \u2014 enabling fully functional AI agents (not just passive text generators) to execute tools locally without internet connectivity or massive memory footprints.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83e\udde0 Deep Dive<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The AI industry keeps pushing toward bigger centralized clusters, convinced that scaling laws demand ever-larger data centers. Liquid AI is taking a different route with LFM2.5-2.6B. By fitting a 2.6-billion parameter model onto something as modest as a Raspberry Pi, the company is freeing advanced agentic workflows from constant GPU reliance. The design draws on efficient neural architectures that deliver useful on-device intelligence without the usual latency, expense, or privacy headaches that come from shipping everything back to a central server.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Edge hardware brings tight limits on memory and power, so Liquid AI leans heavily on optimization. Quantized weights in 4-bit or 8-bit GGUF\/GGML formats let developers squeeze the model into the RAM available on a Raspberry Pi 4 or 5. That change reshapes the total cost of ownership. Instead of paying per token for cloud calls and waiting on API round-trips, teams can run offline agents that handle sensitive data locally. For regulated industries or sites with spotty connectivity, that independence matters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Plenty of reports highlight the \u201cno cloud, no GPU\u201d angle as a win for hobbyists, yet they tend to skip the practical hurdles. Getting stable performance on a Pi still requires careful thermal management and memory tuning. Metrics around tokens per second, power draw, and NPU versus CPU behavior become essential, because sustained local inference can throttle the hardware quickly. The distance between a working demo and a reliable fleet comes down to respecting those constraints and pairing the model with practical frameworks like LangChain or LlamaIndex.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">What sets LFM2.5-2.6B apart from earlier small models is its built-in support for agentic work. A local chatbot is no longer the headline; an autonomous workflow is. The model is tuned for tool use and function calling, so it can read local files, trigger webhooks, or manage home-automation sensors without outside help. That moves edge AI from a passive interface to something that actually executes tasks, opening the door to more resilient, decentralized applications.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">From what I\u2019ve seen, this points to a broader shift in how AI infrastructure will be organized. Routine parsing, local RAG, and tool execution will stay at the edge with models like LFM2.5-2.6B, while only the heaviest reasoning jobs get handed off to frontier systems. For chipmakers and cloud providers, the arrival of capable edge agents introduces a real question about how much inference volume might eventually stay on the device instead of flowing back to the data center.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udcca Stakeholders &amp; Impact<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Stakeholder \/ Aspect<\/th><th>Impact<\/th><th>Insight<\/th><\/tr><\/thead><tbody><tr><td>AI \/ LLM Providers<\/td><td>High<\/td><td>Pressures frontier labs to develop competitive small-parameter edge variants; validates that capable agents don&#8217;t require massive parameter counts.<\/td><\/tr><tr><td>Cloud &amp; GPU Infra (AWS, Nvidia)<\/td><td>Medium\u2013High<\/td><td>Demonstrates a viable path for inference workload cannibalization, shifting compute demand from centralized GPUs to decentralized edge CPUs\/NPUs.<\/td><\/tr><tr><td>Enterprise &amp; Edge Developers<\/td><td>High<\/td><td>Unlocks privacy-first, zero-latency deployments; drastically lowers the TCO for agentic workflows by eliminating per-token API costs.<\/td><\/tr><tr><td>Hardware &amp; IoT (Raspberry Pi, etc.)<\/td><td>Significant<\/td><td>Transforms cheap, ubiquitous single-board computers from passive data collectors into autonomous, AI-driven decision hubs.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\u270d\ufe0f About the analysis<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This independent, research-based analysis evaluates the infrastructure and market impact of Liquid AI&#8217;s LFM2.5-2.6B model. It synthesizes developer pain points, edge deployment benchmarks, and hardware constraints to provide a strategic roadmap for CTOs, embedded engineers, and AI architects evaluating the shift toward decentralized, on-device intelligence.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udd2d i10x Perspective<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Liquid AI is challenging scaling laws from the ground up. While the largest labs chase AGI inside enormous data centers, the nearer commercial opportunity lies in capable, hyper-local intelligence. Freeing agentic workflows from cloud GPUs hints at a future where AI sits closer to the user, more decentralized, and more resilient when connections drop. The next round of infrastructure competition will likely center on <strong>who controls inference at the edge, not only who builds the biggest cluster<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Liquid AI&#8217;s LFM2.5-2.6B runs agentic workflows with tool calling entirely on edge devices like Raspberry Pi. Achieve zero-latency, private AI without cloud APIs or GPUs. Discover the guide.<\/p>\n","protected":false},"author":2,"featured_media":3489,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"paper_title":"","paper_url":"","pdf_url":"","doi":"","arxiv_id":"","authors":"","institutions":"","paper_date":"","venue":"","peer_reviewed":false,"code_url":"","data_url":"","funding_coi":"","figure_license":"","paper_license":"","replication":"","study_models":"","study_sample":"","footnotes":"","_yoast_wpseo_primary_category":""},"categories":[14,11],"tags":[],"companies":[],"products":[],"class_list":["post-3493","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agents","category-infrastructure"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>LFM2.5-2.6B: Liquid AI&#039;s On-Device Agent Model - i10X News<\/title>\n<meta name=\"description\" content=\"Liquid AI&#039;s LFM2.5-2.6B runs agentic workflows with tool calling entirely on edge devices like Raspberry Pi.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LFM2.5-2.6B: Liquid AI&#039;s On-Device Agent Model - i10X News\" \/>\n<meta property=\"og:description\" content=\"Liquid AI&#039;s LFM2.5-2.6B runs agentic workflows with tool calling entirely on edge devices like Raspberry Pi.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model\" \/>\n<meta property=\"og:site_name\" content=\"i10X News\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-07T11:41:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/lfm2.5-2.6b-liquid-ai.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1344\" \/>\n\t<meta property=\"og:image:height\" content=\"768\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Christopher Ort\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@i10x_ai\" \/>\n<meta name=\"twitter:site\" content=\"@i10x_ai\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Christopher Ort\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model\"},\"author\":{\"name\":\"Christopher Ort\",\"@id\":\"https:\\\/\\\/news.i10x.ai\\\/#\\\/schema\\\/person\\\/d410bb7ee72febefca2c9c369939219a\"},\"headline\":\"LFM2.5-2.6B: Liquid AI&#8217;s On-Device Agent Model\",\"datePublished\":\"2026-08-07T11:41:09+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model\"},\"wordCount\":816,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/news.i10x.ai\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/lfm2.5-2.6b-liquid-ai.webp\",\"articleSection\":[\"Agents\",\"Infrastructure\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model#respond\"]}],\"dateModified\":\"2026-08-07T11:41:09+00:00\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model\",\"name\":\"LFM2.5-2.6B: Liquid AI's On-Device Agent Model - i10X News\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/news.i10x.ai\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/lfm2.5-2.6b-liquid-ai.webp\",\"datePublished\":\"2026-08-07T11:41:09+00:00\",\"description\":\"Liquid AI's LFM2.5-2.6B runs agentic workflows with tool calling entirely on edge devices like Raspberry Pi.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model#primaryimage\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/lfm2.5-2.6b-liquid-ai.webp\",\"contentUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/lfm2.5-2.6b-liquid-ai.webp\",\"width\":1344,\"height\":768,\"caption\":\"Abstract representation of a central processor chip with glowing connections, symbolizing edge AI processing, with blurred server racks in the background.\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/lfm2-5-2-6b-liquid-ai-edge-model#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/news.i10x.ai\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"LFM2.5-2.6B: Liquid AI&#8217;s On-Device Agent Model\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/news.i10x.ai\\\/#website\",\"url\":\"https:\\\/\\\/news.i10x.ai\\\/\",\"name\":\"i10X News\",\"description\":\"Stay updated with the latest news and announcements from i10X.\",\"publisher\":{\"@id\":\"https:\\\/\\\/news.i10x.ai\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/news.i10x.ai\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/news.i10x.ai\\\/#organization\",\"name\":\"i10X\",\"url\":\"https:\\\/\\\/news.i10x.ai\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/news.i10x.ai\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/i10xnews.kinsta.cloud\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/i10x-logo-180.png\",\"contentUrl\":\"https:\\\/\\\/i10xnews.kinsta.cloud\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/i10x-logo-180.png\",\"width\":180,\"height\":180,\"caption\":\"i10X\"},\"image\":{\"@id\":\"https:\\\/\\\/news.i10x.ai\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/x.com\\\/i10x_ai\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/i10x-ai\",\"https:\\\/\\\/instagram.com\\\/i10x.ai\",\"https:\\\/\\\/www.youtube.com\\\/@i10X_ai_official\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/news.i10x.ai\\\/#\\\/schema\\\/person\\\/d410bb7ee72febefca2c9c369939219a\",\"name\":\"Christopher Ort\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/christopher-ort-192.webp\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/christopher-ort-192.webp\",\"contentUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/christopher-ort-192.webp\",\"caption\":\"Christopher Ort\"},\"description\":\"Christopher Ort handles SEO, AI agent workflows and PR & partnerships at i10x.ai, the all-in-one AI workspace built by i10X Global Pte. Ltd. in Singapore. He is a graduate of Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU). He is the author of \u201cThe Style Penalty\u201d, a study published on SSRN that analyses 1,576 evaluation decisions by four frontier AI models to show how AI-written CVs fare in automated hiring screens; the research has been covered by heise online and t3n.\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/author\\\/christopher-ort\",\"sameAs\":[\"https:\\\/\\\/i10x.ai\\\/blog\\\/author\\\/christopher-ort\"],\"worksFor\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#organization\"},\"alumniOf\":{\"@type\":\"EducationalOrganization\",\"name\":\"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU)\",\"url\":\"https:\\\/\\\/www.fau.eu\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"LFM2.5-2.6B: Liquid AI's On-Device Agent Model - i10X News","description":"Liquid AI's LFM2.5-2.6B runs agentic workflows with tool calling entirely on edge devices like Raspberry Pi.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model","og_locale":"en_US","og_type":"article","og_title":"LFM2.5-2.6B: Liquid AI's On-Device Agent Model - i10X News","og_description":"Liquid AI's LFM2.5-2.6B runs agentic workflows with tool calling entirely on edge devices like Raspberry Pi.","og_url":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model","og_site_name":"i10X News","article_published_time":"2026-08-07T11:41:09+00:00","og_image":[{"width":1344,"height":768,"url":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/lfm2.5-2.6b-liquid-ai.webp","type":"image\/webp"}],"author":"Christopher Ort","twitter_card":"summary_large_image","twitter_creator":"@i10x_ai","twitter_site":"@i10x_ai","twitter_misc":{"Written by":"Christopher Ort","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"NewsArticle","@id":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model#article","isPartOf":{"@id":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model"},"author":{"name":"Christopher Ort","@id":"https:\/\/news.i10x.ai\/#\/schema\/person\/d410bb7ee72febefca2c9c369939219a"},"headline":"LFM2.5-2.6B: Liquid AI&#8217;s On-Device Agent Model","datePublished":"2026-08-07T11:41:09+00:00","mainEntityOfPage":{"@id":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model"},"wordCount":816,"commentCount":0,"publisher":{"@id":"https:\/\/news.i10x.ai\/#organization"},"image":{"@id":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model#primaryimage"},"thumbnailUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/lfm2.5-2.6b-liquid-ai.webp","articleSection":["Agents","Infrastructure"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model#respond"]}],"dateModified":"2026-08-07T11:41:09+00:00"},{"@type":"WebPage","@id":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model","url":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model","name":"LFM2.5-2.6B: Liquid AI's On-Device Agent Model - i10X News","isPartOf":{"@id":"https:\/\/news.i10x.ai\/#website"},"primaryImageOfPage":{"@id":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model#primaryimage"},"image":{"@id":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model#primaryimage"},"thumbnailUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/lfm2.5-2.6b-liquid-ai.webp","datePublished":"2026-08-07T11:41:09+00:00","description":"Liquid AI's LFM2.5-2.6B runs agentic workflows with tool calling entirely on edge devices like Raspberry Pi.","breadcrumb":{"@id":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model#primaryimage","url":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/lfm2.5-2.6b-liquid-ai.webp","contentUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/lfm2.5-2.6b-liquid-ai.webp","width":1344,"height":768,"caption":"Abstract representation of a central processor chip with glowing connections, symbolizing edge AI processing, with blurred server racks in the background."},{"@type":"BreadcrumbList","@id":"https:\/\/i10x.ai\/news\/lfm2-5-2-6b-liquid-ai-edge-model#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/news.i10x.ai\/"},{"@type":"ListItem","position":2,"name":"LFM2.5-2.6B: Liquid AI&#8217;s On-Device Agent Model"}]},{"@type":"WebSite","@id":"https:\/\/news.i10x.ai\/#website","url":"https:\/\/news.i10x.ai\/","name":"i10X News","description":"Stay updated with the latest news and announcements from i10X.","publisher":{"@id":"https:\/\/news.i10x.ai\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/news.i10x.ai\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/news.i10x.ai\/#organization","name":"i10X","url":"https:\/\/news.i10x.ai\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/news.i10x.ai\/#\/schema\/logo\/image\/","url":"https:\/\/i10xnews.kinsta.cloud\/wp-content\/uploads\/2026\/10\/i10x-logo-180.png","contentUrl":"https:\/\/i10xnews.kinsta.cloud\/wp-content\/uploads\/2026\/10\/i10x-logo-180.png","width":180,"height":180,"caption":"i10X"},"image":{"@id":"https:\/\/news.i10x.ai\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/x.com\/i10x_ai","https:\/\/www.linkedin.com\/company\/i10x-ai","https:\/\/instagram.com\/i10x.ai","https:\/\/www.youtube.com\/@i10X_ai_official"]},{"@type":"Person","@id":"https:\/\/news.i10x.ai\/#\/schema\/person\/d410bb7ee72febefca2c9c369939219a","name":"Christopher Ort","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/christopher-ort-192.webp","url":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/christopher-ort-192.webp","contentUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/christopher-ort-192.webp","caption":"Christopher Ort"},"description":"Christopher Ort handles SEO, AI agent workflows and PR & partnerships at i10x.ai, the all-in-one AI workspace built by i10X Global Pte. Ltd. in Singapore. He is a graduate of Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU). He is the author of \u201cThe Style Penalty\u201d, a study published on SSRN that analyses 1,576 evaluation decisions by four frontier AI models to show how AI-written CVs fare in automated hiring screens; the research has been covered by heise online and t3n.","url":"https:\/\/i10x.ai\/news\/author\/christopher-ort","sameAs":["https:\/\/i10x.ai\/blog\/author\/christopher-ort"],"worksFor":{"@id":"https:\/\/i10x.ai\/news\/#organization"},"alumniOf":{"@type":"EducationalOrganization","name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU)","url":"https:\/\/www.fau.eu"}}]}},"_links":{"self":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts\/3493","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/comments?post=3493"}],"version-history":[{"count":1,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts\/3493\/revisions"}],"predecessor-version":[{"id":5548,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts\/3493\/revisions\/5548"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/media\/3489"}],"wp:attachment":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/media?parent=3493"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/categories?post=3493"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/tags?post=3493"},{"taxonomy":"company","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/companies?post=3493"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/products?post=3493"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}