Liquid AI's LFM3: tiny 2B models run in your browser via WebGPU
Liquid AI has released the LFM3 model family on Hugging Face, with text, vision, audio and omni variants. A demo shows the 2-billion-parameter LFM3-VL-2B describing video in real time right in your browser, with no server at all.
What this is about
The company Liquid AI has published a collection called LFM3 on Hugging Face, the largest platform for openly available AI models. It includes base models and post-trained variants (fine-tuned for tasks such as dialogue or image description). The collection was presented on Reddit, in the r/LocalLLaMA forum, together with a demo: the LFM3-VL-2B model describes video in real time and runs entirely locally in the web browser. The demo comes from the user xenovatech, which is linked to Hugging Face developer Xenova, known for in-browser AI. That attribution rests only on the username, and the source doesn't explain it further.
The source is a model overview and a forum post. It contains no detailed technical information, benchmarks or licensing details. This article therefore sticks to what is documented.
The models in the collection
All listed models have roughly 2 billion parameters. Parameters are, roughly speaking, the knobs a model has tuned during training. 2 billion is considered very small today. Models this size fit on laptops, smartphones and, as it turns out, in a browser.
| Model | Task according to Hugging Face |
|---|---|
| LFM3-1.7B | Text generation |
| LFM3-VL-2B | Image-text-to-text (image understanding) |
| LFM3-Audio-2B | Any-to-Any (various inputs and outputs) |
| LFM3-Omni-2B | Any-to-Any |
| d1-Omni-2B | Audio-text-to-text |
| d1-2B | Image-text-to-text |
The list also contains another 2B text-generation entry whose name is missing from the excerpt. According to the listing, the most popular are LFM3-1.7B (about 6,310 downloads, 127 likes) and LFM3-VL-2B (255 downloads, 52 likes). These numbers are only a snapshot. "Any-to-Any" means a model can process several types of input (such as text, images and sound) and produce several types of output. The source doesn't say what exactly LFM3-Audio and LFM3-Omni can each do, or how "d1" differs from "LFM3."
Many formats for many devices
The number of formats the models come in stands out:
- GGUF: Format for llama.cpp and tools built on it, also usable on CPU.
- ONNX: Open interchange format that browser runtimes such as Transformers.js can use.
- MLX: For Apple silicon Macs (M chips).
- FP8 and NVFP4: Heavily compressed number formats for modern Nvidia GPUs. They save memory and speed up computation. The source lists these two formats for LFM3-1.7B and LFM3-VL-2B.
That lets Liquid AI serve phone, Mac, PC and browser users at the same time.
The browser demo
The demo runs on WebGPU. This is a browser interface that gives web pages access to the graphics card. That way a model can compute on your own device without sending images to a server. The demo shows real-time video captioning: the model continuously describes the camera or video frames. The demo is available as a Hugging Face Space from the "webml-community" group and opens directly in the browser. The source gives no frame rate, no GPU requirements and no measurements. How smoothly it runs depends on your own device.
What you need to try it
- Browser demo: A current browser with WebGPU support and a reasonably modern GPU. The model downloads on first launch. The source doesn't give its size.
- Locally as a program: With 2 billion parameters and 4-bit quantization, roughly 2 to 3 GB of VRAM (video memory) should be enough. That's an estimate, not a vendor figure.
- Mac: The MLX variants are meant for Apple silicon.
- No GPU: The GGUF variants are generally suitable for running on CPU.
- Disk space: A few gigabytes is realistic for a 2B model.
Our take
LFM3 fits the trend toward small, efficient models that run locally. The demo is a vivid proof that image understanding with 2 billion parameters is practical in the browser. What's especially interesting is that privacy comes as a side effect: the video never leaves your device.
A lot remains open. The source gives no license, no benchmarks and no comparison with other small vision-language models. How good the descriptions really are, you can only find out by trying it. If you want to use the models commercially, read the license on the respective model page first. The Liquid AI blog post that the collection links to is also not reproduced in the source.
Sources
- r/LocalLLaMA huggingface.co
- Google News (US) news.google.com
- Product Hunt – KI producthunt.com
- Google News (IN) news.google.com
- Google News (JP) news.google.com
Try it yourself: LFM3-VL-2B
Explained step by step, no prior knowledge needed – on your own computer or free in your browser.
What can I do with it?
LFM3-VL-2B is a model that understands text, images and video (a VLM, or vision-language model). You can show it a photo and ask questions about it. It describes images, reads documents, charts and app screens, and finds objects in a picture. What's new is video input: you give the model a video and it answers questions about it. According to the Model Card, it samples video at 2 frames per second. It also "thinks" step by step before answering (thinking mode). That helps with harder questions, such as math or charts. It can also judge the spatial layout of objects. Everything runs locally, so your images never leave your device. At roughly 1.9 billion parameters, it's small. Liquid AI explicitly advises against using it for coding, long chats or complex reasoning. Neither the article nor the Model Card offers comparisons with other models or benchmarks. How good it really is, you'll have to test yourself.
Who is it worth it for?
Worth a look if you're curious about image understanding without the cloud, if you're a tinkerer or a web developer. It's also worth trying if you want to analyze videos, screenshots or charts. It's less suited as an all-round chatbot, for coding or for tricky reasoning tasks. Don't expect top-tier quality from a 2B model.
Ideas: What you can do with it
Open the browser demo and allow camera access. The model continuously describes what it sees. In a few minutes you'll know how well it grasps scenes, and nothing leaves your machine.
Photograph a sign, a package insert or a menu from your vacation. Ask the model to read out the text and translate it into English. Always double-check important details yourself.
Upload a screenshot of an app or a chart from a report. Ask for the key takeaways or for specific values. It's handy at the office when you need a quick overview.
Film a walkthrough or a repair and ask the model later what was shown. According to the Model Card, lecture recordings and camera clips are also intended use cases. Start with short clips.
Show a photo and ask where something is in the picture and what lies to its left or right. For robotics tinkerers, it's a starting point for testing spatial understanding.
Start llama.cpp as a server. It provides an OpenAI-compatible interface at localhost:8080. You can use it to build your own scripts or web apps that analyze images locally.
What your computer needs
Which variant is right for me?
Many models come in several sizes or "quantizations." These are compressed versions: smaller and faster, but slightly less accurate. Rule of thumb: pick the largest variant whose memory requirement fits in your VRAM (video memory). The scripts below choose this for you automatically.
| Variant | Download | Needs approx. | Compatible graphics cards |
|---|---|---|---|
Q4_K_M Default |
1.1 GB | 2.5 GB | almost any graphics card with 4 GB or more (e.g. GTX 1650, RTX 3050) |
Q5_K_M |
1.3 GB | 2.5 GB | almost any graphics card with 4 GB or more (e.g. GTX 1650, RTX 3050) |
Q6_K |
1.4 GB | 3 GB | almost any graphics card with 4 GB or more (e.g. GTX 1650, RTX 3050) |
Q8_0 |
1.8 GB | 3 GB | almost any graphics card with 4 GB or more (e.g. GTX 1650, RTX 3050) |
BF16 |
3.5 GB | 5 GB | e.g. RTX 2060, RTX 3050 8 GB, RX 6600 |
For a quick look, the browser demo (a Hugging Face Space from webml-community) is enough. It needs no installation. For everyday use, the Model Card recommends GGUF with llama.cpp. The Q4_0 variant is named in its example. The easiest route is the Llama.app for macOS and Windows. On Apple silicon Macs, use the 4-bit MLX variant with MLX-VLM. ONNX suits web projects and cross-platform use. BF16 (the uncompressed weights) is meant for GPU use with Transformers but needs more memory. The article lists FP8 and NVFP4 for modern Nvidia cards. As a rough estimate (not a vendor figure), 4-bit needs about 2 to 3 GB of VRAM (video memory).
- The browser demo uses WebGPU, a browser interface for the graphics card. You need a browser with WebGPU support and a reasonably modern GPU. The source gives no frame rate or minimum requirements.
- For 4-bit, 2 to 3 GB of VRAM (video memory) is plausible by rough estimate. That's not a vendor figure.
- According to the Model Card, GGUF runs on CPUs and GPUs. So it works without a graphics card, but is likely to be slower.
- MLX (4-bit) is meant for Apple silicon Macs (M chips).
- The context length is up to 128K tokens (input plus output). Long videos and many images fill the context and cost memory. Start with short clips.
- For video input with llama.cpp, ffmpeg must be on your PATH. It's a program that splits videos into individual frames.
How do I find out what my computer has?
- Windows: Press Ctrl + Shift + Esc → Task Manager → Performance tab → click GPU on the left. At the bottom you'll see Dedicated GPU memory (that's your VRAM). You'll find system memory under Memory.
- Mac: Apple menu at the top left → About This Mac. You'll see your chip (e.g. M2) and memory (e.g. 16 GB). On Apple chips, graphics and processor share this memory.
- Linux: Type
nvidia-smiin the terminal (shows your graphics card and its memory) andfree -hfor system memory.
Guide
Google Colab (in your browser, no installation)
Google Colab is a free service from Google. It gives you a computer with a graphics card in your browser for a few hours. All you need is a Google account. For this model: runs for free – The free Tesla T4 GPU with 15 GB is enough.
- Download the notebook. Click the button and save the file.
- Open Colab. Go to colab.research.google.com and sign in with your Google account.
- Upload the notebook. In the window that appears, click Upload on the left (or choose File → Upload notebook at the top) and select the file
colab.ipynb. - Turn on the GPU. In the top menu, go to Runtime → Change runtime type → under Hardware accelerator, select T4 GPU → Save.
- Start everything. Runtime → Run all (keyboard shortcut Ctrl + F9). If you see the warning “This notebook was not authored by Google,” click Run anyway.
- Wait. The cells run one after another. A green checkmark appears to the left of each cell when it's done. Installation and download take about 4 minutes here.
- Try it out. The result appears below the last cell. To test something else, change the text in the cell and run it again with Shift + Enter. The last cell starts a proper chat with an input field.
- Clean up. When you're done: Runtime → Disconnect and delete runtime. This saves your free quota.
Good to know: The free GPU is limited (usually a few hours per day). After about 90 minutes of inactivity, Colab disconnects, and you'll have to start everything over.
Windows 10 / 11
The easiest way is Ollama, a free program that launches AI models with a single command.
- Update your graphics driver (NVIDIA cards only): Install the NVIDIA app or the latest driver. For AMD cards, use the latest Adrenalin driver.
- Install Ollama. Download the file OllamaSetup.exe from ollama.com/download/windows, double-click it, and choose Install. Afterward, a small llama icon appears in the taskbar at the bottom right.
- Open a terminal window. Press the Windows key, type PowerShell and press Enter. A blue or black window opens.
- Start the model. Copy this command ("Copy" button), paste it into the window with a right-click, and press Enter:
The first time, Ollama downloads the model (1.1 GB (about 1 min at 100 Mbit/s)). After that, it's instant.ollama run hf.co/LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M - Start typing. Once
>>> Send a messageappears, type your question and press Enter. Type/byeto end the chat.
Not enough memory? Pick a smaller variant from the table above, e.g. ollama run hf.co/LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M.
Prefer automatic? A script that picks the right variant for you
- Download both files into the same folder: try_windows.ps1 demo.py
- Open the folder in File Explorer, right-click an empty spot and choose Open in Terminal.
- Paste this command and press Enter:
powershell -ExecutionPolicy Bypass -File try_windows.ps1
If needed, the script installs Ollama via winget, measures your VRAM (video memory) and downloads the matching variant.
macOS
Macs with an Apple chip (M1 to M5) are ideal. Older Intel Macs are very slow.
- Install Ollama. Download it from ollama.com/download/mac, open the file, and drag Ollama into your Applications folder. Launch Ollama once – a llama appears in the menu bar at the top.
- Open Terminal. Press Cmd + Space, type Terminal, and press Enter.
- Start the model. Paste the command (Cmd + V) and press Enter:
The first time, the model is downloaded (1.1 GB (about 1 min at 100 Mbit/s)).ollama run hf.co/LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M - Start typing. Type your question and press Enter. Quit with
/bye.
Linux
- Check your GPU.
nvidia-smishould show your card. If it doesn't, install the driver on Ubuntu and restart:sudo ubuntu-drivers install - Install Ollama:
curl -fsSL https://ollama.com/install.sh | sh - Start the model:
Exit withollama run hf.co/LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M/bye. Useollama psto check whether the model is running on the GPU. - Or let it pick the right variant automatically:
bash try_linux.sh # mit Python/transformers statt Ollama: MODE=hf bash try_linux.sh
Try it
Describe in two sentences what you see in this image.Which objects are on the desk in the photo? List them.Read out the text on this sign and translate it into English.What does this chart show? Name the highest and lowest values and briefly explain the trend.This video shows a walkthrough of my apartment. Which rooms can you see, and what is in each one?Tips for better results
- Use the sampling settings recommended in the Model Card: temperature 0.1, top_p 1.0, top_k 50, min_p 0.0, repetition_penalty 1.1. They keep answers calm and factual.
- Ask one clear question per image or video. According to the Model Card, the model isn't meant for long multi-turn conversations. If needed, start a new chat.
- For hard questions (math, charts, multiple steps), the model thinks first. That takes longer but often gives better answers. Allow some waiting time.
- You can talk to the model in German. German is among the supported languages. For tricky texts, it helps to say explicitly: "Answer in German."
- Use well-lit, sharp images and give short clips for videos. The model processes images at their original resolution.
If something goes wrong
The license is the "LFM Open License v1.0." Read it on the model page before using the model commercially.
The demo won't run without a WebGPU-capable browser. Update your browser or try a different one.
The model downloads on first launch. That can take a while depending on your connection, and the source doesn't give the size.
Pick the format that matches your program: GGUF for llama.cpp, MLX for Macs, ONNX for ONNX Runtime. Otherwise the model won't load.
For llama.cpp, the Model Card requires version b11960 or newer. Older builds probably won't work. For Transformers, you need version 5.21.0 or newer.
According to the Model Card, vLLM and SGLang don't fully support LFM3-VL yet (SGLang under review, vLLM in progress). Start with llama.cpp or Transformers.
Videos with llama.cpp need ffmpeg on your PATH, or you'll get an error.
With Transformers, generate() needs an explicit token limit (max_new_tokens), because the thinking phase can run long.
“out of memory,” a crash, or your computer freezes
The model is too big for your VRAM (video memory). Pick a smaller variant from the table above and close other programs, especially games and browsers with lots of tabs.
It runs, but extremely slowly
Your CPU is probably doing the work instead of your graphics card. In Ollama, the PROCESSOR column of ollama ps shows how much runs on the GPU. If it says "100% CPU", update your graphics driver or pick a smaller variant that fits entirely into VRAM (video memory).
“ollama” or “python” is not found
Close the terminal window and open it again so it recognizes the newly installed program. For Python on Windows: reinstall it and check Add python.exe to PATH.
Windows: "Running scripts is disabled on this system"
Run the script with exactly the command above: powershell -ExecutionPolicy Bypass -File try_windows.ps1. This allows execution for this one time only.
Error message when loading the model (version, "trust_remote_code", unknown architecture)
Brand-new models often need the latest software version. With Ollama: update Ollama. With Python: pip install -U transformers in the activated environment. The Model Card usually lists the exact version.
Tokenstrom generates the scripts and notebook automatically. Please take a quick look before running them – they're plain, readable text. Mind the model's license: custom license (see Model Card).
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