6 releases covered on ThursdAI · liquid.ai ↗
Liquid AI ships LFM2.5-350M with agentic tool calling at 350M params
Liquid AI released LFM2.5-350M, a 350M-parameter open model that does agentic tool calling and fits under 500MB quantized. It targets edge and on-device agent workloads where tiny deployable models matter.
Liquid AI releases LFM2-24B-A2B, a laptop-friendly 24B MoE
Liquid AI released LFM2-24B-A2B, a 24B mixture-of-experts model with only 2.3B active parameters that runs on consumer laptops. The panel highlighted its speed and surprisingly strong non-coding reasoning, reinforcing the trend of efficient low-active-parameter open models for local use.
Liquid AI's LFM2.5-1.2B-Thinking: on-device reasoning under 900MB
Liquid AI released LFM2.5-1.2B-Thinking, a 1.2B parameter reasoning model that runs entirely on-device with under 900MB of memory. Its hybrid architecture with gated convolutions delivers 239 tokens/sec on an AMD CPU and 82 tokens/sec on a mobile NPU, making it practical for edge devices, Raspberry Pi, and older iPhones.
Liquid AI LFM 2.5: 1B on-device family with end-to-end audio
Liquid AI released LFM 2.5, a family of ~1.2B parameter on-device models spanning text, vision, and audio, announced at CES alongside AMD's Lisa Su. The models hit 239 tokens/sec on AMD CPU and 100 tokens/sec on iPhone 16 Pro Max, and include a revolutionary end-to-end audio model that skips the traditional ASR-LLM-TTS pipeline entirely, running in as little as 8GB of RAM.
Liquid AI ships LFM2-VL-3B tiny multilingual vision-language model
Liquid AI released LFM2-VL-3B, a tiny multilingual vision-language model, part of a wave of OCR-and-VLM releases this week. It targets efficient on-device and edge vision-language workloads at the 3B scale.
Liquid AI ships Liquid Nanos, tiny task-specific on-device models
Liquid AI released Liquid Nanos, a family of very small task-specific models built for jobs like extraction, translation, RAG, and tool calling that can run on-device. The collection landed on Hugging Face, fitting the episode's theme of small-but-capable models powering real products.
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