Liquid AI details LFM3 multimodal models for single-GPU specialization

In a post on Hugging Face, Liquid AI presents LFM3 as six roughly 2B-parameter models sharing a backbone across text, vision, audio, omni and structured decisions, with single-GPU fine-tuning for every model (source). That approach could let developers specialize a modality-specific model rather than deploy a larger generalist.

Liquid AI reports that the text model fits in about 1 GB at 4-bit precision and supports a 256K-token context window; the memory figure is precision-specific, not an established full-workload memory budget (source).

Liquid AI also reports robotics action generation below 50 ms specifically on Jetson AGX Orin with TensorRT FP16, so that result does not establish performance on other devices (source). These are vendor claims, not independent confirmation. Target-device latency, long-context memory requirements and reliability after task-specific tuning remain unverified.