{"id":75694,"date":"2026-03-06T12:10:57","date_gmt":"2026-03-06T12:10:57","guid":{"rendered":"https:\/\/youzum.net\/liquid-ai-releases-localcowork-powered-by-lfm2-24b-a2b-to-execute-privacy-first-agent-workflows-locally-via-model-context-protocol-mcp\/"},"modified":"2026-03-06T12:10:57","modified_gmt":"2026-03-06T12:10:57","slug":"liquid-ai-releases-localcowork-powered-by-lfm2-24b-a2b-to-execute-privacy-first-agent-workflows-locally-via-model-context-protocol-mcp","status":"publish","type":"post","link":"https:\/\/youzum.net\/ja\/liquid-ai-releases-localcowork-powered-by-lfm2-24b-a2b-to-execute-privacy-first-agent-workflows-locally-via-model-context-protocol-mcp\/","title":{"rendered":"Liquid AI Releases LocalCowork Powered By LFM2-24B-A2B to Execute Privacy-First Agent Workflows Locally Via Model Context Protocol (MCP)"},"content":{"rendered":"<p>Liquid AI has released <strong>LFM2-24B-A2B<\/strong>, a model optimized for local, low-latency tool dispatch, alongside <strong>LocalCowork<\/strong>, an open-source desktop agent application available in their <a href=\"https:\/\/github.com\/Liquid4All\/cookbook\/tree\/main\/examples\/localcowork\" target=\"_blank\" rel=\"noreferrer noopener\">Liquid4All GitHub Cookbook<\/a>. The release provides a deployable architecture for running enterprise workflows entirely on-device, eliminating API calls and data egress for privacy-sensitive environments.<\/p>\n<h3 class=\"wp-block-heading\"><strong>Architecture and Serving Configuration<\/strong><\/h3>\n<p>To achieve low-latency execution on consumer hardware, LFM2-24B-A2B utilizes a Sparse Mixture-of-Experts (MoE) architecture. While the model contains 24 billion parameters in total, it only activates approximately 2 billion parameters per token during inference.<\/p>\n<p>This structural design allows the model to maintain a broad knowledge base while significantly reducing the computational overhead required for each generation step. <strong>Liquid AI stress-tested the model using the following hardware and software stack:<\/strong><\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Hardware:<\/strong> Apple M4 Max, 36 GB unified memory, 32 GPU cores.<\/li>\n<li><strong>Serving Engine:<\/strong> <code>llama-server<\/code> with flash attention enabled.<\/li>\n<li><strong>Quantization:<\/strong> <code>Q4_K_M GGUF<\/code> format.<\/li>\n<li><strong>Memory Footprint:<\/strong> ~14.5 GB of RAM.<\/li>\n<li><strong>Hyperparameters:<\/strong> Temperature set to 0.1, top_p to 0.1, and max_tokens to 512 (optimized for deterministic, strict outputs).<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>LocalCowork Tool Integration<\/strong><\/h3>\n<p>LocalCowork is a completely offline desktop AI agent that utilizes the Model Context Protocol (MCP) to execute pre-built tools without relying on cloud APIs or compromising data privacy, logging every action to a local audit trail. The system includes 75 tools across 14 MCP servers capable of handling tasks like filesystem operations, OCR, and security scanning. However, the provided demo focuses on a highly reliable, curated subset of 20 tools across 6 servers, each rigorously tested to achieve over 80% single-step accuracy and verified multi-step chain participation.<\/p>\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-4-3 wp-has-aspect-ratio\">\n<div class=\"wp-block-embed__wrapper\">\n<\/div>\n<\/figure>\n<p>LocalCowork acts as the practical implementation of this model. It operates completely offline and comes <strong>pre-configured with a suite of enterprise-grade tools:<\/strong><\/p>\n<ul class=\"wp-block-list\">\n<li><strong>File Operations:<\/strong> Listing, reading, and searching across the host filesystem.<\/li>\n<li><strong>Security Scanning:<\/strong> Identifying leaked API keys and personal identifiable information (PII) within local directories.<\/li>\n<li><strong>Document Processing:<\/strong> Executing Optical Character Recognition (OCR), parsing text, diffing contracts, and generating PDFs.<\/li>\n<li><strong>Audit Logging:<\/strong> Recording every tool call locally for compliance tracking.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>Performance Benchmarks<\/strong><\/h3>\n<p>Liquid AI team evaluated the model against a workload of 100 single-step tool selection prompts and 50 multi-step chains (requiring 3 to 6 discrete tool executions, such as searching a folder, running OCR, parsing data, deduplicating, and exporting).<\/p>\n<h4 class=\"wp-block-heading\"><strong>Latency<\/strong><\/h4>\n<p>The model averaged <strong>~385 ms per tool-selection response<\/strong>. This sub-second dispatch time is highly suitable for interactive, human-in-the-loop applications where immediate feedback is necessary.<\/p>\n<h4 class=\"wp-block-heading\"><strong>Accuracy<\/strong><\/h4>\n<ul class=\"wp-block-list\">\n<li><strong>Single-Step Executions:<\/strong> 80% accuracy.<\/li>\n<li><strong>Multi-Step Chains:<\/strong> 26% end-to-end completion rate.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>Key Takeaways<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li><strong>Privacy-First Local Execution:<\/strong> LocalCowork operates entirely on-device without cloud API dependencies or data egress, making it highly suitable for regulated enterprise environments requiring strict data privacy.<\/li>\n<li><strong>Efficient MoE Architecture:<\/strong> LFM2-24B-A2B utilizes a Sparse Mixture-of-Experts (MoE) design, activating only ~2 billion of its 24 billion parameters per token, allowing it to fit comfortably within a ~14.5 GB RAM footprint using <code>Q4_K_M GGUF<\/code> quantization.<\/li>\n<li><strong>Sub-Second Latency on Consumer Hardware:<\/strong> When benchmarked on an Apple M4 Max laptop, the model achieves an average latency of ~385 ms for tool-selection dispatch, enabling highly interactive, real-time workflows.<\/li>\n<li><strong>Standardized MCP Tool Integration:<\/strong> The agent leverages the Model Context Protocol (MCP) to seamlessly connect with local tools\u2014including filesystem operations, OCR, and security scanning\u2014while automatically logging all actions to a local audit trail.<\/li>\n<li><strong>Strong Single-Step Accuracy with Multi-Step Limits:<\/strong> The model achieves 80% accuracy on single-step tool execution but drops to a 26% success rate on multi-step chains due to \u2018sibling confusion\u2019 (selecting a similar but incorrect tool), indicating it currently functions best in a guided, human-in-the-loop loop rather than as a fully autonomous agent.<\/li>\n<\/ul>\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n<p>Check out the\u00a0<strong><a href=\"https:\/\/github.com\/Liquid4All\/cookbook\/tree\/main\/examples\/localcowork\" target=\"_blank\" rel=\"noreferrer noopener\">Repo<\/a> <\/strong>and<strong> <a href=\"https:\/\/www.liquid.ai\/blog\/no-cloud-tool-calling-agents-consumer-hardware-lfm2-24b-a2b\" target=\"_blank\" rel=\"noreferrer noopener\">Technical details<\/a>.\u00a0<\/strong>Also,\u00a0feel free to follow us on\u00a0<strong><a href=\"https:\/\/x.com\/intent\/follow?screen_name=marktechpost\" target=\"_blank\" rel=\"noreferrer noopener\"><mark>Twitter<\/mark><\/a><\/strong>\u00a0and don\u2019t forget to join our\u00a0<strong><a href=\"https:\/\/www.reddit.com\/r\/machinelearningnews\/\" target=\"_blank\" rel=\"noreferrer noopener\">120k+ ML SubReddit<\/a><\/strong>\u00a0and Subscribe to\u00a0<strong><a href=\"https:\/\/www.aidevsignals.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">our Newsletter<\/a><\/strong>. Wait! are you on telegram?\u00a0<strong><a href=\"https:\/\/t.me\/machinelearningresearchnews\" target=\"_blank\" rel=\"noreferrer noopener\">now you can join us on telegram as well.<\/a><\/strong><\/p>\n<p>The post <a href=\"https:\/\/www.marktechpost.com\/2026\/03\/05\/liquid-ai-releases-localcowork-powered-by-lfm2-24b-a2b-to-execute-privacy-first-agent-workflows-locally-via-model-context-protocol-mcp\/\">Liquid AI Releases LocalCowork Powered By LFM2-24B-A2B to Execute Privacy-First Agent Workflows Locally Via Model Context Protocol (MCP)<\/a> appeared first on <a href=\"https:\/\/www.marktechpost.com\/\">MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>Liquid AI has released LFM2-24B-A2B, a model optimized for local, low-latency tool dispatch, alongside LocalCowork, an open-source desktop agent application available in their Liquid4All GitHub Cookbook. The release provides a deployable architecture for running enterprise workflows entirely on-device, eliminating API calls and data egress for privacy-sensitive environments. Architecture and Serving Configuration To achieve low-latency execution on consumer hardware, LFM2-24B-A2B utilizes a Sparse Mixture-of-Experts (MoE) architecture. While the model contains 24 billion parameters in total, it only activates approximately 2 billion parameters per token during inference. This structural design allows the model to maintain a broad knowledge base while significantly reducing the computational overhead required for each generation step. Liquid AI stress-tested the model using the following hardware and software stack: Hardware: Apple M4 Max, 36 GB unified memory, 32 GPU cores. Serving Engine: llama-server with flash attention enabled. Quantization: Q4_K_M GGUF format. Memory Footprint: ~14.5 GB of RAM. Hyperparameters: Temperature set to 0.1, top_p to 0.1, and max_tokens to 512 (optimized for deterministic, strict outputs). LocalCowork Tool Integration LocalCowork is a completely offline desktop AI agent that utilizes the Model Context Protocol (MCP) to execute pre-built tools without relying on cloud APIs or compromising data privacy, logging every action to a local audit trail. The system includes 75 tools across 14 MCP servers capable of handling tasks like filesystem operations, OCR, and security scanning. However, the provided demo focuses on a highly reliable, curated subset of 20 tools across 6 servers, each rigorously tested to achieve over 80% single-step accuracy and verified multi-step chain participation. LocalCowork acts as the practical implementation of this model. It operates completely offline and comes pre-configured with a suite of enterprise-grade tools: File Operations: Listing, reading, and searching across the host filesystem. Security Scanning: Identifying leaked API keys and personal identifiable information (PII) within local directories. Document Processing: Executing Optical Character Recognition (OCR), parsing text, diffing contracts, and generating PDFs. Audit Logging: Recording every tool call locally for compliance tracking. Performance Benchmarks Liquid AI team evaluated the model against a workload of 100 single-step tool selection prompts and 50 multi-step chains (requiring 3 to 6 discrete tool executions, such as searching a folder, running OCR, parsing data, deduplicating, and exporting). Latency The model averaged ~385 ms per tool-selection response. This sub-second dispatch time is highly suitable for interactive, human-in-the-loop applications where immediate feedback is necessary. Accuracy Single-Step Executions: 80% accuracy. Multi-Step Chains: 26% end-to-end completion rate. Key Takeaways Privacy-First Local Execution: LocalCowork operates entirely on-device without cloud API dependencies or data egress, making it highly suitable for regulated enterprise environments requiring strict data privacy. Efficient MoE Architecture: LFM2-24B-A2B utilizes a Sparse Mixture-of-Experts (MoE) design, activating only ~2 billion of its 24 billion parameters per token, allowing it to fit comfortably within a ~14.5 GB RAM footprint using Q4_K_M GGUF quantization. Sub-Second Latency on Consumer Hardware: When benchmarked on an Apple M4 Max laptop, the model achieves an average latency of ~385 ms for tool-selection dispatch, enabling highly interactive, real-time workflows. Standardized MCP Tool Integration: The agent leverages the Model Context Protocol (MCP) to seamlessly connect with local tools\u2014including filesystem operations, OCR, and security scanning\u2014while automatically logging all actions to a local audit trail. Strong Single-Step Accuracy with Multi-Step Limits: The model achieves 80% accuracy on single-step tool execution but drops to a 26% success rate on multi-step chains due to \u2018sibling confusion\u2019 (selecting a similar but incorrect tool), indicating it currently functions best in a guided, human-in-the-loop loop rather than as a fully autonomous agent. Check out the\u00a0Repo and Technical details.\u00a0Also,\u00a0feel free to follow us on\u00a0Twitter\u00a0and don\u2019t forget to join our\u00a0120k+ ML SubReddit\u00a0and Subscribe to\u00a0our Newsletter. Wait! are you on telegram?\u00a0now you can join us on telegram as well. The post Liquid AI Releases LocalCowork Powered By LFM2-24B-A2B to Execute Privacy-First Agent Workflows Locally Via Model Context Protocol (MCP) appeared first on MarkTechPost.<\/p>","protected":false},"author":2,"featured_media":75695,"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 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