Liquid AI

What is Liquid AI?

Liquid AI develops device-native foundation models, called Liquid Foundation Models (LFM), designed to run on phones, laptops, cars and other edge hardware instead of only in data centers. The vendor reports 57+ shipped variants and about 49.6 million downloads.

Key capabilities of Liquid AI

• LFM2.5 language models: sizes from 230M to 24B parameters
• Vision-language models: LFM2.5-VL in 450M and 3B variants
• Accelerated variants: LFM2.5-VL-DSpark versions
• Quantized builds: for example Q4_0 for constrained hardware
• Many runtimes: llama.cpp, MLX, ONNX, CoreML, SGLang and vLLM
• Model playground: test models in the browser

How Liquid AI works

You pick an LFM size and format, download it from Hugging Face or use the model playground, and run it locally through a supported runtime. Design priorities are latency, privacy and fit to hardware limits. Enterprise deployments are arranged through sales.

Who uses Liquid AI?

Developers and device makers shipping on-device AI. The vendor lists Shopify, Mercedes-Benz, MacPaw and Qualcomm Snapdragon devices among partners.

Liquid AI pricing

Liquid AI does not publish pricing on its homepage. Enterprise solutions go through sales, and models can be accessed on Hugging Face.

Liquid AI alternatives

NVIDIA NeMo is a toolkit for building and customizing models, DeepSpeed is a training optimization library, and Vast.ai, DeepInfra and Nebius are GPU or inference hosting providers. Liquid AI differs by supplying edge-sized models.