Liquid LFM3 Is INSANE: Tiny Local AI That ANYONE Can Run!
Explore Liquid AI’s LFM3 models and discover how lightweight AI can handle text, images, audio and video while running locally on affordable hardware. A look at the future of accessible, offline AI.
Liquid LFM3 Is INSANE: Tiny Local AI That ANYONE Can Run!
The world of AI is becoming increasingly powerful, but many of the latest models require expensive computers, powerful GPUs and cloud-based services to operate. Liquid AI is taking a different approach with its new LFM3 family of models, bringing surprisingly capable AI features to smaller devices that ordinary users can actually afford. The LFM3 family focuses on making AI smaller, faster and more accessible, allowing users to run AI directly on their own computers, laptops and even affordable single-board computers without needing an internet connection.
Meet the Liquid LFM3 Family
The LFM3 release introduces several specialised models, each designed for a different type of AI task. Most are built around approximately 2 billion parameters, making them considerably smaller than many of today's popular AI models.
The main models include:
LFM3 Text: A 1.7-billion-parameter language model designed for everyday text conversations, instructions and general tasks. It also supports a large 256K-token context window, allowing it to process lengthy amounts of information.
LFM3 Vision: Allows users to upload images and ask questions about what appears in them. It can recognise objects, understand images and extract text from pictures.
LFM3 Audio: Designed to understand spoken language, transcribe audio and support voice-based interactions.
LFM3 Omni: Combines multiple capabilities, including text, images, video and audio understanding.
LFM3 Decision: A specialised model designed for decision-making tasks.
Another important detail is that Liquid AI also released base model weights, giving developers the option to fine-tune the models for specific applications.
Surprisingly Capable for Such Small Models
One of the most interesting aspects of LFM3 is how much these relatively small models can accomplish. During testing, the 1.7B text model was connected to Hermes Agent, allowing it to interact with files and perform tasks through an AI agent environment. It successfully located files on the computer, made changes to a basic browser operating system interface and saved the updated files. It also demonstrated the ability to use different tools to research information online and produce a structured report. Although it struggled with some more complicated instructions and made occasional mistakes, its performance was impressive for a model of this size. This is particularly interesting because smaller AI models could make it easier for developers and hobbyists to experiment with multiple AI agents running simultaneously. Instead of needing expensive hardware to operate large models, users could potentially run several smaller agents on an ordinary computer.
LFM3 Vision: Understanding Images Locally
The vision model brings another interesting capability to the LFM3 family. During testing, it was given several different images and asked questions about their contents. It successfully identified a dog breed, explained parts of a car-related meme and recognised an older laptop. It also demonstrated OCR (Optical Character Recognition), which is the ability to extract written information from images. One particularly interesting test involved photographing a car service manual and asking the model to convert information about fuses and relays into a table. The model successfully extracted much of the information, although it missed a few details. While it isn't as accurate as much larger vision models, the results show how useful a small local AI model could become for everyday tasks, especially when running directly on a smartphone or computer.
An Offline AI Voice Assistant
The LFM3 Audio model takes things further by allowing users to interact with AI through voice. In the demonstration, the model was connected to a custom voice interface that allowed conversations without manually pressing a button every time the user wanted to speak. It could understand spoken questions, respond verbally and maintain a back-and-forth conversation. However, the testing also revealed some limitations. It occasionally misunderstood spoken information and even invented details during a conversation. The important part is that it demonstrated the possibility of running a voice-based AI assistant locally, without relying on cloud services or sending every request to an external server. This could eventually make affordable offline voice assistants much easier to build.
LFM3 Omni: Bringing Multiple AI Capabilities Together
The Omni model is designed to understand different types of information, including text, images, video and audio. During testing, it was given a short video and asked questions about its contents. It responded quickly and identified several elements correctly, although some of its answers were inaccurate. The demonstration also showed that the exact capabilities depend on how the model is integrated into the application. Nevertheless, having a small model capable of processing multiple types of media opens up interesting possibilities for applications that need to understand videos, images and audio without relying entirely on cloud-based AI.
Running AI on an $80 Computer
Perhaps the biggest highlight of the entire demonstration was getting LFM3 running on an affordable edge device. The test used an Arduino Uno Q with a Qualcomm Dragonwing processor and just 4GB of memory. The 1.7B model was running using a 4-bit quantised version, which reduces the amount of memory required to operate it. The setup demonstrated a completely local AI chatbot running on inexpensive hardware, with no internet connection required. It achieved approximately three tokens per second, meaning it was relatively slow but still functional. This is important because it shows that local AI doesn't necessarily require an expensive gaming PC or dedicated graphics card. Even relatively inexpensive hardware can now run models capable of understanding instructions and responding to questions.
What Makes LFM3 Different?
The biggest advantage of Liquid AI's LFM3 family isn't necessarily that it outperforms the largest AI models. Instead, it's about making useful AI capabilities accessible to more people.
Here are some of the biggest advantages:
Low hardware requirements: Small enough to run on ordinary computers and selected edge devices.
Offline functionality: Models can operate locally without requiring a constant internet connection.
Multiple capabilities: Text, image, audio and video-related AI functionality.
Developer flexibility: Available model weights allow developers to experiment and fine-tune models.
Affordable experimentation: Makes it easier for hobbyists and developers to build local AI applications.
There are also limitations worth understanding. These models are not designed to replace much larger AI systems, and their smaller size means they can struggle with complicated reasoning, coding and detailed instructions. Another important consideration is licensing. The models use a proprietary licence rather than common permissive licences such as MIT or Apache 2.0. Developers should therefore check the licence conditions before using them in commercial projects.
Final Thoughts
Liquid AI's LFM3 family is an exciting development for anyone interested in local AI. While these models won't replace the most powerful AI systems available today, they demonstrate just how much smaller models can accomplish with limited hardware. The ability to run text, vision and audio models on ordinary computers, and even experiment with AI on an affordable $80 device, makes this release particularly interesting. The real achievement of LFM3 is accessibility. Instead of requiring expensive hardware and powerful cloud infrastructure, it gives more people the opportunity to experiment with AI directly on their own devices. Local AI is becoming increasingly practical, and Liquid AI's LFM3 family is another example of how the technology is moving beyond expensive hardware and into the hands of everyday users.