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Get startedCactus vs Liquid AI: Inference Engine vs Efficient Model Provider
Cactus is an inference engine that runs models on-device with cloud fallback. Liquid AI is a research company building efficient foundation models (LFM series) designed for edge deployment. They serve complementary roles: Cactus provides the runtime, while Liquid AI provides the models. In fact, Cactus already supports Liquid AI's LFM2 models natively.
Cactus
Cactus is a hybrid AI inference engine for mobile, desktop, and edge hardware. It provides the runtime layer that loads, quantizes, and executes AI models on-device with automatic cloud fallback. Cactus supports models from many providers including Gemma, Qwen, and Liquid AI's LFM series, with sub-120ms latency and cross-platform SDKs.
Liquid AI
Liquid AI is a research-focused AI company building efficient foundation models. Their LFM2 and LFM2.5 models are designed for high performance on resource-constrained hardware. Liquid AI offers cloud API endpoints and SDK access but relies on third-party runtimes like Cactus for on-device mobile deployment.
Feature comparison
Performance & Latency
Cactus delivers sub-120ms latency through zero-copy memory mapping and optimized quantization. Liquid AI's models are architecturally efficient, using novel techniques to achieve strong accuracy at smaller parameter counts. The combination of Liquid AI's efficient models running inside Cactus's optimized runtime produces excellent on-device performance.
Model Support
Cactus is model-agnostic and supports Gemma 3/4, Qwen 3, LFM2, Whisper, Moonshine, Parakeet, and more across LLM, transcription, vision, and embeddings. Liquid AI focuses on its own LFM2 language models and LFM2-VL vision-language models. They are complementary: Liquid AI builds efficient models, Cactus runs them on-device.
Platform Coverage
Cactus runs on iOS, Android, macOS, Linux, watchOS, and tvOS with SDKs for Swift, Kotlin, Flutter, React Native, Python, C++, and Rust. Liquid AI primarily offers macOS and Linux support through Python, with mobile deployment requiring a third-party runtime. For mobile deployment of LFM models, Cactus provides the missing runtime layer.
Pricing & Licensing
Cactus is MIT licensed with optional cloud API pricing. Liquid AI offers a free-tier cloud API with enterprise plans for heavier usage. Their models are available on HuggingFace. Running LFM models inside Cactus on-device is free after the initial download, making the combination cost-effective.
Developer Experience
Cactus provides a unified API to load and run models across modalities, abstracting away hardware details. Liquid AI's Python SDK targets ML practitioners and server-side developers. Cactus's SDKs are designed for app developers. The two tools serve different developer personas but work well together for teams wanting efficient models on mobile.
Strengths & limitations
Cactus
Strengths
- Hybrid routing automatically falls back to cloud when on-device confidence is low
- Single unified API across LLM, transcription, vision, and embeddings
- Sub-120ms on-device latency with zero-copy memory mapping
- Cross-platform SDKs for Swift, Kotlin, Flutter, React Native, Python, C++, and Rust
- NPU acceleration on Apple devices for significantly faster inference
- Up to 5x cost savings on hybrid inference compared to cloud-only
Limitations
- Newer project compared to established frameworks like TensorFlow Lite
- Qualcomm and MediaTek NPU support still in development
- Cloud fallback requires API key configuration
Liquid AI
Strengths
- Highly efficient model architectures designed for edge deployment
- Strong research team pushing state-of-the-art efficiency
- Vision-language multimodal capabilities
- Models optimized for low-resource environments
Limitations
- Primarily a model provider, not a deployment framework
- No native mobile SDKs
- No built-in on-device runtime or hybrid routing
- Requires third-party runtimes for mobile deployment
The Verdict
Cactus and Liquid AI are more complementary than competitive. Liquid AI builds efficient foundation models, while Cactus provides the runtime to deploy them on mobile and edge devices. If you need an on-device inference engine, choose Cactus. If you need efficient models for any runtime, explore Liquid AI's LFM series. For the best mobile experience, run LFM models inside Cactus.
Frequently asked questions
Can I run Liquid AI models on Cactus?+
Yes. Cactus natively supports Liquid AI's LFM2 models. You can run LFM2 language models and LFM2-VL vision-language models on-device through Cactus's inference engine on any supported platform.
Is Liquid AI a model provider or an inference engine?+
Liquid AI is primarily a model provider and research company. It builds efficient foundation models (LFM series) and offers cloud API access. For on-device mobile deployment, you need a separate runtime like Cactus.
Does Liquid AI have mobile SDKs?+
No. Liquid AI provides a Python SDK for cloud and desktop use. For iOS and Android deployment of LFM models, you would use a mobile inference engine like Cactus, which supports LFM2 natively.
Which is better for edge AI deployment?+
They serve different purposes. Cactus is the deployment runtime with hybrid routing and mobile SDKs. Liquid AI provides efficient models suited for edge hardware. Together they form a complete edge AI stack.
Does Cactus support transcription while Liquid AI does not?+
Correct. Cactus supports Whisper, Moonshine, and Parakeet transcription models with under 6% WER. Liquid AI focuses on language and vision models without dedicated speech-to-text capabilities.
Which has better hybrid cloud support?+
Cactus offers confidence-based automatic cloud fallback, seamlessly routing requests when on-device inference quality is low. Liquid AI offers cloud API endpoints but no automatic on-device-to-cloud routing mechanism.
Try Cactus today
On-device AI inference with automatic cloud fallback. One unified API for LLMs, transcription, vision, and embeddings across every platform.