Introduction
Liquid AI is an edge-first AI company delivering on-device foundation models (LFM2), an OS- and model-agnostic developer platform (LEAP), and a private mobile chat app (Apollo). Their technology is built around "liquid neural networks" — dynamical-system-inspired architectures optimized for sequential and multimodal data, low-latency inference, and high compute efficiency.
Key features
- Liquid Foundation Models (LFM2): on-device, compute-efficient models designed for reasoning, multimodal tasks, and constrained hardware.
- LEAP platform: developer-first, OS-agnostic runtime and tooling for deploying, benchmarking, and optimizing models across edge devices and servers.
- Apollo mobile app & Playground: private local chat interfaces and developer sandbox for testing LFMs on device.
- Integrations & distribution: available via Hugging Face, AWS Marketplace (Bedrock), Lambda Labs and other cloud/edge partners.
- Focus on efficiency: optimized inference beyond traditional transformers, real-time adaptation, and reduced memory footprint.
Technical highlights
- Architecture: liquid neural networks inspired by dynamical systems and signal processing for flexible, temporal processing.
- Deployment targets: mobile phones, laptops (including AMD Ryzen/AI), embedded devices, edge servers, and cloud fallbacks.
- APIs & tools: public Playground, model listings via partner clouds, and LEAP SDK for model customization and deployment.
Use cases
- Private on-device conversational agents (secure, low-latency chat).
- Vision-language applications (LFM2-VL) for mobile/edge inference.
- Robotics and embedded systems needing adaptive, real-time models.
- Enterprises seeking compute-efficient foundation models for offline or privacy-sensitive applications.
- Developers prototyping and shipping edge AI with LEAP and integrated SDKs.
Target users
- ML engineers and researchers interested in efficient on-device models.
- Mobile and embedded developers building private AI experiences.
- Enterprises and hardware partners optimizing models for specific devices.
Liquid AI's unique selling point is bringing foundation-model capabilities to devices without cloud dependency, combining new neural architectures with developer tooling to make on-device AI practical and performant.
