Fastest on-device foundation models.

Purpose-built for edge AI, our hybrid models run entirely on-device and shine at agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations.

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State-of-the-art performance at every scale*.

Benchmarks
LFM2-350M
LFM2-700M
LFM2-1.2B
MMLU
(5-shot)
43.43
49.9
55.23
GPQA
(0-shot)
27.46
28.48
31.47
IFEval
65.12
72.23
74.89
IFBENCH
16.41
20.56
20.7
GSM8K
(0-shot)
30.1
46.4
58.3
MGSM
(5-shot)
29.52
45.36
55.04
MMMLU
(5-shot)
37.99
43.28
46.73
*We evaluated LFM2 across seven popular benchmarks covering knowledge (5-shot MMLU, 0-shot GPQA), instruction following (IFEval, IFBench), mathematics (0-shot GSM8K, 5-shot MGSM), and multilingualism (5-shot OpenAI MMMLU, 5-shot MGSM again) with seven languages (Arabic, French, German, Spanish, Japanese, Korean, and Chinese).

Built for efficient inference at the edge.

Unmatched speed, quality, and memory efficiency on the edge.

Whether deploying on smartphones, laptops, or vehicles, LFM2 runs efficiently on CPU, GPU, and NPU hardware.  Designed for millisecond latency, offline resilience, and data privacy, LFM2 unlocks the full potential of edge devices across industries.

Phone

Automotive

Drones

Fig. 1. Prefill performance on CPU in ExecuTorch
Fig. 1. Prefill performance on CPU in ExecuTorch
Fig. 2. Prefill performance on CPU in ExecuTorch
Fig. 2. Prefill performance on CPU in ExecuTorch

Achieve best performance by fine-tuning LFM2 on your use case

The power of model customization, directly into your hands.

Our full-stack solution includes architecture, optimization, and deployment engines to accelerate the path from prototype to product.

Start building with LFMs today

Try LEAP, our developer-first platform for AI on the edge, proudly OS- and model-agnostic.

Frequently asked questions.

As an enterprise, can we purchase full local access to LFMs?
Can we fine-tune LFMs?
Where can I learn more about Liquid Foundation Models?
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