Liquid AI Inc., an artificial intelligence startup building AI models with a novel architecture that provides high performance for size, today announced a breakthrough in AI training and customization for extremely small models capable of delivering OpenAI GPT-4o-level capability for specialized tasks.
The new models, called “Nanos,” run between 350 million and 2.6 billion parameters, small enough to run locally on phones, laptops and embedded devices.
Small and compact frontier large language models enable users to perform AI tasks directly on their devices, eliminating the need for cloud support. By processing all AI inference locally, users can enjoy AI-driven insights and conversational capabilities while ensuring their privacy is maintained.
“Nanos flip the deployment model,” said Liquid AI Chief Executive Ramin Hasani. “Instead of shipping every token to a data center, we ship intelligence to the device. That unlocks speed, privacy, resilience and a cost profile that finally scales to everyone.”
Liquid said Nanos, despite their tiny size, approach reliability on a frontier level and provide the foundation for agentic AI capabilities on device with precise data extraction, structured output generation, multilingual transition across more than 10 languages, retrieval-augmented generation or RAG support, mathematical reasoning and tool calling capabilities.
The company is launching seven task-specific Nanos in the initial release, with plans to expand the family. They include LFM2-350M-ENJP-MT, a Japanese-English translation model that can run on smartphones, and LFM-350M-Extract, a multilingual data extraction model that can be used to pull information from invoice emails and format it into JSON, a common data structure for exchange.
Liquid said both 350 million-parameter models surpass the quality of generalist open-source models more than 10 times their size. LFM2-350M-ENJP-MT has also been shown to deliver Japanese and English translation on par with GPT-4o, a model estimated to be more than 500 times larger.
The company trained the Japanese translation model on a broad range of text, including chat messages, multi-paragraph news articles, technical papers and formal writing. Liquid noted it was evaluated on the public llm-jp-eval benchmarks, which primarily consists of short translations of one- to two-sentence articles from Wikipedia and single-sentence news items.
In the 1.2 billion-parameter size, the company released an extraction model, a model designed for answering questions based on large amounts of information using RAG, a technique used to retrieve the most relevant, up-to-date information from external knowledge sources, and a function-calling model.
In one example, Nano LFM2-1.2B-Extract, the larger version of the 350M model, can output complex data objects in different languages with better performance than Google LLC’s Gemma 3 27B, a model more than 20 times its size, and is competitive in performance with GPT-4o. It’s also small enough to run on most current-generation smartphones.
By developing ultra-small, high-performance task-specific models, Liquid said, it’s providing a support network for AI agents running on devices. Instead of relying on a single, high-power generalist AI, an app can outsource its task-specific needs to smaller, more energy-efficient models to support intelligence and automation.
“Liquid Nanos provide the task-specific performance of large frontier models at zero marginal inference cost,” said Chief Technology Officer Mathias Lechner. “Our enterprise customers have successfully deployed Liquid Nanos in scenarios ranging from high-throughput cloud instances at massive scale to running fully local on low-power embedded devices.”
Liquid’s models are built using a specialized architecture based on the concept of “liquid neural networks,” a classification of AI networks that differs from generative pretrained transformer-based models, or GPTs, that are the foundation for today’s popular chatbots such as ChatGPT and Gemini. The company stated that this architecture enables its model to deliver performance that is comparable to, or even better than, traditional LLMs currently available on the market.
“Liquid’s Nanos represents a powerful inflection point for AI PCs, delivering frontier-level performance in a compact, energy-efficient form,” said Mark Papermaster, chief technology officer and executive vice president at Advanced Micro Devices Inc.
Papermaster added that AMD believes on-device intelligence is key to scaling AI broadly with sustainability in mind. Because on-device AI models don’t require power-hungry data centers to perform, the more processing that happens on smartphones, laptops and PCs, the less overall energy they consume.
The company said Liquid Nanos are not available on the Liquid Edge AI Platform, LEAP, for download and integration on iOS and Android mobile phones and laptops. Developers can also access the new models on Hugging Face and use them directly out of the box with a broad license for academics, developers and small businesses.
Images: SiliconANGLE/Microsoft Designer, Liquid AI