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52M visits/mo+0.7%#4 Coding & Development
Liquid AI
Device-native foundation models and toolchain for fine-tuning once and running anywhere on edge devices like phones, laptops, and cars via llama.cpp, MLX, ONNX, and CoreML.
$10Starting price
124KMonthly visits
35Popularity
About Liquid AI
Liquid AI builds device-native foundation models (LFMs) designed to run locally across phones, laptops, vehicles, and embedded systems. These compact models are exportable to multiple runtimes including llama.cpp, MLX, ONNX, and CoreML, enabling private, low-latency reasoning and search with over 3,200 variants and proven industrial deployments. The platform is built for engineers shipping AI into products beyond the data center, serving mobile app developers, automotive software teams, embedded and robotics engineers, and ML practitioners in sectors like e-commerce, finance, healthcare, industrial, and defense. Researchers can prototype liquid neural networks, state-space models, and tokenizer upgrades, then validate with Pipette benchmarking. Organizations in regulated environments adopt LFMs to keep sensitive content local while maintaining interactive responsiveness expected on consumer hardware. The workflow involves selecting an LFM variant sized for target hardware, fine-tuning with LEAP on domain data, quantizing, and exporting a single artifact per runtime. Deployment targets include laptops, mobiles, and vehicles with private on-device inference and monitoring through existing application stacks. Documentation and example projects support bring-up on Apple Silicon and CPU-only endpoints, simplifying integration for heterogeneous fleets.
Liquid AI
- Founder
- Daniela L. Rus
Key features
- Device-native foundation models (LFMs) with 3,200+ variants
- Export to llama.cpp, MLX, ONNX, CoreML, SGLang, and vLLM
- Fine-tuning with LEAP SDK on proprietary data
- Quantization-aware distillation and tokenizer expansion
- Private, low-latency on-device inference and monitoring
- Pipette benchmarking for validation and research
- Multimodal understanding with LFM2.5-VL for text and image grounding
- DSpark for inference acceleration and edge deployment
- Support for CPU-only, Apple Silicon, GPUs, and automotive hardware
- Deterministic runtime artifacts for packaging and reproducibility
Use cases
- Deploy in-car assistants running fully offline and privacy-preserving
- Build on-device customer support copilots for mobile apps
- Enable factory inspection with vision-language reasoning at the edge
Pros
- Supports deployment across heterogeneous hardware including phones, laptops, vehicles, and embedded systems
- Offers over 3,400 model variants optimized for edge deployment with proven industrial use cases
- Enables private, on-device inference with no data leaving the device, suitable for regulated environments
- Provides multiple runtime export options such as llama.cpp, MLX, ONNX, and CoreML for flexibility
- Includes a full-stack solution with fine-tuning (LEAP SDK), quantization, and deployment tools
Cons
- May require technical expertise to integrate and optimize models for specific hardware targets
- Limited to device-native models, which may not suit all cloud-centric or high-compute use cases
- Fine-tuning and deployment workflows may involve complexity for non-technical users
Frequently asked questions about Liquid AI
What are Liquid AI's device-native foundation models (LFMs)?
Liquid AI's LFMs are compact, general-purpose AI models designed to run locally on edge devices such as phones, laptops, vehicles, and embedded systems. They prioritize low latency, privacy, and hardware efficiency while supporting a wide range of applications.
Who should use Liquid AI's platform?
The platform is built for engineers and teams shipping AI into products beyond data centers, including mobile app developers, automotive software teams, embedded and robotics engineers, and ML practitioners in sectors like e-commerce, finance, healthcare, industrial, and defense.
What runtimes does Liquid AI support for deployment?
Liquid AI models can be exported to multiple runtimes including llama.cpp, MLX, ONNX, CoreML, SGLang, and vLLM, enabling deployment across diverse hardware and software environments.
How does the pricing model work for Liquid AI?
Liquid AI's foundation models are free to download, run, and fine-tune for companies with annual revenue under $10 million. For larger organizations, enterprise plans include commercial licenses, bespoke optimization, and dedicated support.
Can I fine-tune Liquid AI models for my specific use case?
Yes, the LEAP SDK allows users to fine-tune LFMs on domain-specific data, quantize the models, and export them for deployment, streamlining the path from prototype to production.
Does Liquid AI provide tools for benchmarking on-device models?
Yes, Liquid AI offers Pipette, a benchmarking suite designed to measure and compare the performance of on-device intelligence across different hardware configurations and model variants.
Liquid AI traffic & engagement
liquid.ai · third-party estimates from Similarweb
Data as of Sep 2026Full AI website ranking →
Monthly visits · Sep 2026124K-21.4% MoM12-month high 192K (Jun 2026)
Rank among AI websites#1,781▼ 205 vs last monthof 8,652 tools on aitoolnavio™
Top marketUnited States33.8%Top 5 countries bring 52% of visits
Average visit45sLonger than 53% of AI sites
Pages per visit2.15More than 62% of AI sites
Bounce rate41%Leave after one page · lower is better
Similarweb global rank#321,166#246,044 in United States
Traffic trend
Last 4 months · grows by one month every month
- Jun 2026192K
- Jul 2026147K−23.4%
- Aug 2026157K+6.8%
- Sep 2026124K−21.4%
Where visitors come from
Share of visits by channel
No measurable traffic from Paid Referrals.
Top countries
Share of visits · estimated visits
Other countries: 47.9%
Visits sent by AI assistants
How people reach this site from other AI tools
875 a month · 0.7% of all visits
Compared with similar tools
Coding & Development · Sep 2026
Traffic figures are third-party estimates, good for scale and trends; they can differ from a company's own numbers. Updated monthly.
