Liquid AI, a startup pursuing alternatives to the popular "transformer"-based AI models that have come to define the generative AI era, is announcing not one, not two, but a whole family of six different types of AI models called Liquid Nanos that it says are better suited to the "reality of most AI deployments" in enterprises and organizations than the larger foundation models from rivals like OpenAI, Google, and Anthropic.
Liquid Nanos are task-specific foundation models that range from 350 million to 2.6 billion parameters, targeted towards enterprise deployments — basically, you can set and forget these things on enterprise-grade, field devices from laptops to smartphones to even sensor arrays and small robots.
According to the company, Liquid Nanos deliver performance that rivals far larger models on specialized, agentic workflows such as multilingual data extraction, translation, retrieval-augmented (RAG) question answering, low-latency tool and function calling, math reasoning, and more.
It's an alternative approach to what we've seen so far from the "agentic AI" wave that many companies have sought to ride lately. Typically, a company will use a larger foundation model like the kinds offered by Google (with Gemini), OpenAI (GPT-5 and o-series), Anthropic (Claude) or others, and try to focus it narrowly on a task or set of tasks using prompting, caching, and memory.
Of course, this still relies on maintaining (and paying for access) to these more powerful, centralized models on cloud servers — and thus, these agents will suffer in areas with poor internet connectivity or for privacy and security-focused applications, and have more limitations on how the underlying models can be modified or customized.
By shifting computation onto devices rather than relying on cloud infrastructure, Liquid Nanos aim to improve speed, reduce costs, enhance privacy, and enable applications in enterprise and research-grade environments where connectivity or energy use is constrained.
The company calls this out directly in its blog post, stating:
"In our society, people won’t be using one general assistant. Instead, they will rely on multiple small, task-specific agents that live across their devices (phone, laptop, car, watch, home hub), apps (mail, calendar, documents, browser, shopping, travel, finance), and the services they interact with (each bank/telco/retailer/healthcare provider often runs per-user agents for personalization, support, risk, and compliance). Add ephemeral micro-agents spun up during workflows (RAG fetchers, extractors, translators, tool-callers) and background security/automation agents, and you quickly reach ~100 agents per person."
Task-specific offerings
The first set of models in the Liquid Nanos lineup are designed for specialized use cases:
LFM2-Extract: multilingual models (350M and 1.2B parameters) optimized for extracting structured data from unstructured text, such as converting emails or reports into JSON or XML.
LFM2-350M-ENJP-MT: a 350M parameter model for bidirectional English toJapanese translation, trained on a broad range of text types.
LFM2-1.2B-RAG: a 1.2B parameter model tuned for retrieval-augmented generation (RAG) pipelines, enabling grounded question answering over large document sets.
LFM2-1.2B-Tool: a model specialized for precise tool and function calling, designed to run with low latency on edge devices without relying on longer reasoning chains.
LFM2-350M-Math: a reasoning-oriented model aimed at solving challenging math problems efficiently, with reinforcement learning techniques used to control verbosity.
Luth-LFM2 series: community-developed fine-tunes by Sinoué Gad and Maxence Lasbordes, specializing in French while preserving English capabilities.
These models target specific tasks where small, fine-tuned architectures can match or even outperform generalist systems more than 100 billion parameters in size.
Evaluation results
Liquid AI reports that its extraction models outperform much larger systems in benchmarks measuring syntax validity, accuracy, and faithfulness.
The LFM2-1.2B-Extract model, for instance, produces structured outputs in multiple languages at a level that exceeds Gemma 3 27B, a model more than 20 times its size.
For translation, the LFM2-350M-ENJP-MT model was evaluated using the llm-jp-eval benchmark and showed competitive performance against GPT-4o. Similarly, the RAG-focused 1.2B model was tested against peers of similar size and found to be competitive across groundedness, relevance, and helpfulness.
Community-driven contributions also show measurable gains. The Luth-LFM2 series improves French benchmark scores while also raising performance on English tasks, demonstrating that targeted specialization can enhance cross-lingual results.
Availability and licensing
Liquid Nanos are available immediately. Developers can download and deploy them through the Liquid Edge AI Platform (LEAP) on iOS, Android, and laptops, as well as through Hugging Face.
Integration options include SDKs and modular composition into multi-agent systems.
They're all made available under Liquid AI's custom LFM Open License v1.0. It's not fully open source, and will be reminiscent to those familiar with Meta's licensing terms for its Llama AI models.
The LFM Open License v1.0 gives developers broad rights to use, modify, and distribute Liquid AI’s models, with a key condition tied to revenue. Individuals, researchers, nonprofits, and companies making under $10 million annually can use the models for both research and commercial purposes free of charge. Redistribution and derivative works are allowed so long as attribution is maintained, modifications are documented, and license terms are included. The license also includes patent rights from contributors, though these terminate if the user initiates patent litigation.
For enterprises above the $10 million annual revenue threshold, the license does not grant rights to commercial use. Larger organizations must negotiate separate commercial agreements with Liquid AI if they wish to deploy the models in products or services.
In practice, this means startups, academics, and smaller companies can adopt and experiment with Liquid’s models without barriers, while larger enterprises face licensing costs or restrictions before scaling deployments.
Liquid AI is also working with Fortune 500 partners in industries such as consumer electronics, automotive, e-commerce, and finance to provide customized deployments.
In addition, the Apollo mobile app, available for free on the Apple App Store, allows users to experiment with the models on-device.
Why small models matter
Over the last several years, advances in AI have largely been powered by increasingly large models hosted in data centers. This has allowed broad improvements in language understanding and reasoning but comes with trade-offs in cost, latency, and privacy. For many use cases, transmitting every request to the cloud is impractical.
Liquid AI frames Nanos as a reversal of this model: instead of shipping data to frontier-scale systems, intelligence is shipped to the device. The models run within 100MB to 2GB of memory, making them feasible for deployment on modern mobile hardware.
Ramin Hasani, Liquid AI’s co-founder and CEO, a former AI Scientist at the Massachusetts Institute of Technology's esteemed Computer Science & Artificial Intelligence Laboratory, placed the launch in the wider context of rising infrastructure costs.
He pointed out that more than a trillion dollars is expected to be invested in data centers by 2027, but the economics of such spending are uncertain unless efficiency keeps improving.
In his view, the more sustainable approach is hybrid, with lightweight inference handled on devices and only the most demanding tasks escalated to the cloud.
"With Nanos delivering frontier-level results on specialized tasks, and running locally on devices, this is our first step toward planet-scale, device-cloud AI that is both accessible and economically sane," he wrote on X. "Let’s move the median token to the edge!"
Company origins and earlier releases
Liquid AI was founded by former researchers from MIT CSAIL to develop foundation models beyond the transformer-based architecture that underpins most large language models.
From the outset, the company positioned itself as an alternative to transformer dominance, building models from principles rooted in dynamical systems, signal processing, and numerical linear algebra.
The company’s first public release came in September 2024 with the debut of Liquid Foundation Models (LFMs). These non-transformer models were offered in three parameter sizes—1.3B, 3B, and a 40B Mixture-of-Experts variant—and quickly attracted attention for outperforming similarly sized transformer systems.
In April 2025, Liquid AI extended its architectural experimentation with Hyena Edge, a convolution-based hybrid model designed for smartphones and edge deployments.
July 2025 marked the launch of LFM2, the company’s second-generation architecture. Designed to deliver the fastest on-device inference in its class, LFM2 achieved up to 2x faster decode and prefill speeds on CPUs compared to Qwen3 and provided 3x training efficiency over the previous generation.
To support adoption, Liquid AI simultaneously introduced the Liquid Edge AI Platform (LEAP), a cross-platform SDK for iOS and Android that simplifies deploying small models locally. LEAP allows developers to integrate compact models with minimal machine learning expertise, while its companion iOS app, Apollo, enables offline testing. Together, the tools reflected the company’s emphasis on decentralizing AI execution away from cloud dependence.
Across these releases, Liquid AI has consistently advanced an approach centered on efficiency and adaptability. From its initial non-transformer LFMs to specialized edge-focused models, the company has sought to demonstrate that small, fast models can rival much larger systems while making AI more accessible across devices and industries.
Looking ahead
The launch of Liquid Nanos underscores a shift in AI deployment strategy toward smaller, more specialized systems that can operate independently of the cloud.
While the industry race to scale up model sizes continues, Liquid AI is betting that compact, task-oriented models will enable broader adoption and new classes of applications, particularly in settings where privacy, latency, or cost present significant barriers.