The Rise of Liquid Nanos: Rethinking Agentic AI
What if less is the future of powerful AI?
Imagine your phone, laptop, car, and smart home each running dozens of tiny AIs — all zeroing in on specific tasks — instead of shoving every request into a giant cloud-model. That might sound counterintuitive in an age obsessed with ever-bigger foundation models. Yet MIT spin-off Liquid AI argues exactly that: we may have gotten “agentic AI” wrong all along by overemphasizing monolithic models. Their answer? Liquid Nanos — compact, task-specific models built for the edge.
From “one model to rule them all” → “many tiny agents”
In many current agentic AI systems, the pattern is:
- Take a generalist foundation model (e.g. GPT, Claude, Gemini)
- Narrow its behavior via prompting, memory, caching, fine-tuning
- Serve multiple tasks via the same large model
That works — until latency, cost, privacy, connectivity, or customization become constraints. Liquid AI proposes flipping that paradigm: ship intelligence to devices rather than shipping data to remote clouds. ([Venturebeat][1])
Liquid’s “Nanos” are models ranging from ~350 million to 2.6 billion parameters, each specialized for a task (e.g. data extraction, translation, tool invocation, math). ([Venturebeat][1]) Because they’re lightweight, they can run on field devices, laptops, even sensors and robots, without needing constant connectivity. ([Venturebeat][1])
What kinds of Nanos are already available?
Liquid AI rolled out six task-oriented models in its Liquid Nanos lineup:
- LFM2-Extract (350M / 1.2B): structured data extraction from unstructured text
- LFM2-350M-ENJP-MT: English ↔ Japanese translation
- LFM2-1.2B-RAG: optimized for retrieval-augmented question answering
- LFM2-1.2B-Tool: precision in tool / function calling
- LFM2-350M-Math: math reasoning with controlled verbosity
- Luth-LFM2 series: community fine-tunes (e.g. French) while preserving English capabilities ([Venturebeat][1])
Despite their small sizes, some Nanos outperform much larger models in benchmarks of accuracy, faithfulness, and syntactic validity. For example, the extraction model outperformed the much larger “Gemma 3 27B” in structured output tests. ([Venturebeat][1])
The licensing, deployment & trade-offs
- Liquid Nanos can be downloaded and deployed via the Liquid Edge AI Platform (LEAP) (iOS, Android, laptops). ([Venturebeat][1])
- They’re also available on Hugging Face under a custom LFM Open License v1.0. ([Venturebeat][1])
- The license is free for individuals, researchers, nonprofits, and companies with < $10M revenue (with attribution and documentation requirements). Larger enterprises must negotiate separate commercial terms. ([Venturebeat][1])
Liquid AI’s philosophy is that many lightweight, specialized models lead to better latency, better privacy, lower cost, and more flexibility — especially in domains with connectivity or resource constraints. ([Venturebeat][1])
Why Liquid Nanos Matter—and Why the AI World Should Listen
- Latency & connectivity resilience Large, cloud-hosted AI can lag when networks are slow or unavailable. Tiny on-device models bypass that.
- Privacy & data control Sensitive data doesn’t have to leave the device.
- Cost efficiency Running inference on small models is much cheaper long-term than always hitting a large model in the cloud.
- Modularity & specialization Instead of one “jack-of-all-trades” model, you get multiple masters of small tasks.
- Scalability across devices Whether it’s phones, edge sensors, or robotics, these models can scale into constrained environments.
In short: while the AI industry chases scale by making models bigger, Liquid AI bets on scale by distribution — pushing intelligence outward, not upward.
Glossary
| Term | Definition |
|---|---|
| Agentic AI | AI systems composed of agents (autonomous modules) that act, reason, or intervene toward goals, often via tools, memory, or decision logic. |
| Foundation model | A large, general-purpose AI model (e.g. GPT, PaLM, Claude) typically pre-trained on large datasets and then adapted for specific tasks. |
| Parameter / parameter count | The number of internal weights in a model; more parameters often (though not always) mean greater capacity. |
| Retrieval-Augmented Generation (RAG) | A technique where a model augments its reasoning by fetching relevant external documents or data to ground responses. |
| Inference latency | The time delay between input and output in a model performing its computation. |
| Edge / on-device inference | Running model computations directly on the local device (phone, sensor, IoT) rather than in remote cloud servers. |
Source: What if we’ve been doing agentic AI all wrong? MIT offshoot Liquid AI offers new small, task-specific Liquid Nano models — VentureBeat ([Venturebeat][1])
| [1]: https://venturebeat.com/ai/what-if-weve-been-doing-agentic-ai-all-wrong-mit-offshoot-liquid-ai-offers “What if we’ve been doing agentic AI all wrong? MIT offshoot Liquid AI offers new small, task-specific Liquid Nano models | VentureBeat” |
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