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Liquid AI provides Liquid Foundation Models, a library of efficient multimodal AI models intended for on-device, cloud, and hybrid deployment. The website describes the company as building models optimized for CPUs, GPUs, and NPUs, with use cases that include privacy-critical, low-latency, and security-critical applications. The listed model catalog includes 20 models across text, vision-language, audio, and nano categories. This makes Liquid AI more of an AI infrastructure and model provider than a simple chatbot product.
The provided website schema lists several model offers at a price of $0 USD, including entries such as LFM2-350M, LFM2-700M, LFM2-8B-A1B, LFM2-24B-A2B, and LFM2.5-1.2B-Base. However, the scraped content does not include a complete pricing page with all commercial tiers, enterprise support pricing, usage-based API rates, or deployment fees. For this directory entry, pricing should be treated as free for listed model offers and custom for broader enterprise usage. Organizations should confirm licensing, hosting, support, and production terms directly with Liquid AI.
Liquid AI says its models are optimized for CPUs, GPUs, and NPUs. That is important because many AI deployments depend on non-cloud environments such as laptops, phones, embedded systems, vehicles, or enterprise-controlled hardware. The website positions the models for on-device, cloud, and hybrid deployment rather than only centralized GPU inference. Teams should still test the exact model size, memory usage, and latency on their target hardware before committing.
The website schema lists 20 Liquid Foundation Models in the complete library. Examples from the provided content include LFM2-350M, LFM2-700M, LFM2-8B-A1B, LFM2-24B-A2B, and LFM2.5-1.2B-Base. The catalog spans text, vision-language, audio, and nano models, which suggests Liquid AI is building a model family rather than a single flagship model. This variety is useful for teams that need to match model size and modality to device constraints.
Liquid AI is most relevant for teams that need efficient models deployed close to the user or inside controlled infrastructure. If the priority is privacy-critical, low-latency, or security-critical inference on CPUs, GPUs, or NPUs, Liquid AI fits better than a cloud-only assistant workflow. OpenAI, Anthropic, and Gemini may be better choices for teams that primarily want mature hosted APIs, broad ecosystem tooling, or general-purpose assistant capabilities. Based on our analysis of 870+ AI tools, Liquid AI should be evaluated as deployment-focused model infrastructure rather than a general productivity assistant.
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Tutorial updated March 2026