Liquid AI

Liquid AI released DSpark draft model checkpoints for three LFM2.5 models, enabling speculative decoding that speeds up inference by up to 3.18x on H100 GPUs and 2.87x on-device, with day-one support for llama.cpp and SGLang.

Liquid AI has released LFM2.5-VL-3B, a multimodal upgrade to its LFM2-VL-3B model built for on-device grounding, object detection, and document OCR. The model runs at 228 tokens/sec on an Apple M5 Max and 116 tokens/sec on an AMD Ryzen AI Max+ 395, using under 3.3 GB of memory.

Liquid AI has released LFM2.5-VL-3B, a 3.1-billion-parameter vision-language model built for on-device deployment. The model claims leading performance in its size class on grounding, screen understanding, and tool use, while running at 228 tokens/s on an Apple M5 Max.

Liquid AI has released LFM2.5-2.6B, a 2.6B-parameter model trained on 34 trillion tokens with a 128K context window, built for on-device agentic workloads. The company claims it is competitive with models four times its size on tool use and instruction following.

Liquid AI has released LFM2.5-2.6B, a 2.6B-parameter model designed to run capable tool-calling agents locally on laptops and phones. The company claims it matches or beats models up to 4x its size on instruction-following and tool-use benchmarks while running under 2.5GB of memory.

Liquid AI released two open-weight encoder models, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, built for classification and routing tasks at long context on CPU hardware. The company claims the smaller model runs 3.7x faster than ModernBERT-base at 8,192 tokens while matching or beating larger encoders on GLUE, SuperGLUE, and multilingual benchmarks.

Liquid AI has released LFM2.5-230M, a 230M parameter hybrid model trained on 19 trillion tokens with a 32,768 token context window. The model achieves 213 tok/s decode speed on Galaxy S25 Ultra and 42 tok/s on Raspberry Pi 5, with support for function calling and data extraction tasks.

Liquid AI has released LFM2.5-8B-A1B, an 8-billion parameter hybrid model designed specifically for edge AI and on-device deployment. The model is available in multiple GGUF quantized formats ranging from 4-bit (4.84 GB) to 16-bit (16.9 GB), optimized for memory efficiency.