Liquid AI's LFM2 Just Dropped — Here's How to Run It on Vast.ai

Liquid AI has introduced LFM2-24B-A2B, a groundbreaking 24 billion parameter foundation model that activates only 2.3 billion parameters per token, making it one of the most aggressively sparse mixture-of-experts models available. This model diverges from traditional transformer-only models by employing a hybrid architecture that incorporates gated short convolution blocks instead of predominantly using attention layers, with only 10 out of 40 layers utilizing grouped query attention while the rest are convolutional. This innovative design allows the model to maintain competitive performance across various benchmarks such as MMLU-Pro and GPQA Diamond, despite its low active parameter count. Liquid AI, stemming from MIT, has scaled this architecture from 350M to 24B parameters, following log-linear scaling laws, and the LFM2-24B-A2B stands as the first in its family to be broadly applicable. The model's deployment is facilitated by day-one support for vLLM, SGLang, and llama.cpp, and it can be efficiently run on platforms like Vast.ai due to its compact weight of approximately 48 GB at BF16, negating the need for quantization or multi-GPU setups.

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