ThursdAI — the weekly AI news podcast hosted by Alex Volkov — has covered 11 Liquid AI releases since Sep 2025, most recently Pipette on Aug 27, 2026. Highlights include LFM2.5-2.6B, LFM2.5-VL-3B, LFM2.5 QAD checkpoints, LFM2.5-1.2B-Thinking. All 11 shipped with open weights. Every entry below has the episode segment where we covered it live, plus primary-source links and key numbers where we have them.
Liquid AI releases Pipette, an open-source on-device eval suite
Liquid AI released Pipette, an open-source evaluation suite for on-device models. Noted in the newsletter TL;DR; the segment didn't make the published episode cut.
Liquid AI ships LFM2.5 QAD 4-bit checkpoints for edge devices
Liquid AI released quantization-aware-distilled 4-bit checkpoints for LFM2.5 models from 230M to 2.6B parameters, retaining roughly 97% of BF16 quality with 3x faster decode on edge devices.
Liquid AI LFM2.5-VL-3B runs 228 tok/s on M5 Max in ~3GB
Liquid AI released LFM2.5-VL-3B, a small vision-language model that runs at 228 tok/s on an M5 Max in roughly 3GB of memory. Weights are on Hugging Face.
Liquid's LFM2.5-2.6B: agentic RL trained inside real harnesses, running in 1.7GB on a phone
A 2.69B-parameter hybrid model pre-trained on ~34T tokens whose post-training ran agentic RL inside real harnesses (Hermes Agent, OpenClaw, Pi), so tool calling was learned where tool calling happens. It beats Qwen3.5-9B, three times its size, on ToolSandbox and instruction following, runs 220 tok/s on an M5 Max CPU and fits in ~1.7GB at Q4 on a phone. Liquid's own model card honestly scopes it away from agentic coding and knowledge-heavy work: this is for private, on-device agents.
Liquid AI open-sources Antidoom, removing the reasoning doom-loop
An open method that suppresses the failure mode where reasoning models spiral into repetitive degenerate output: doom-loop rates dropped from 22.9% to 1% on Qwen3.5-4B and from 10.2% to 1.4% on an LFM2.5 checkpoint, with eval scores improving across the board.
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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