AI/LLM · Hybrid
Liquid AI
Liquid AI builds and serves its own Liquid Foundation Models (LFM and LFM2) with a custom hybrid liquid/convolution/attention architecture, optimized for both data-center and fully on-device deployment across CPUs, GPUs, and NPUs.[1][3][4][9][11] Public materials emphasize hardware-in-the-loop training and edge/PC deployment, but do not expose a full production cloud stack, suggesting a mix of self-hosted/model-serving infrastructure plus OEM/partner integrations rather than a single public hyperscaler.[9][11]
Foundation proximity: Native (own / self-hosted weights)
Foundational models & how they consume them
PrimaryLFM2-2.6B· Liquid AI (self-hosted / OEM on-device) · Native (own / self-hosted weights)
SecondaryLFM 1.3B· Liquid AI (self-hosted / edge-optimized) · Native (own / self-hosted weights)
TertiaryLFM 3.1B· Liquid AI (self-hosted / edge-optimized) · Native (own / self-hosted weights)
TertiaryLFM 40.3B MoE· Liquid AI (self-hosted / data-center) · Native (own / self-hosted weights)
Cloud & datastore
HybridCompliance
—Similar companies
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FAQ
Does Liquid AI use a foundational AI model?
Liquid AI uses LFM2-2.6B, LFM 1.3B, LFM 3.1B, LFM 40.3B MoE (Native (own / self-hosted weights)).
What cloud does Liquid AI run on?
Liquid AI runs on Hybrid.
How close does Liquid AI run to the foundational model?
Liquid AI is Native (own / self-hosted weights) — close to the metal, so cheaper tokens and more control over outputs.
Sources
liquid.ai ↗Also on the map
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Architecture inferred from public sources · confidence low · verify before betting on a detail.