AI & Machine Learning

Liquid AI's New LFM2-24B-A2B Hybrid Architecture Blends Attention with Convolutions to Solve the Scaling Bottlenecks of Modern LLMs

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Liquid AI introduced LFM2-24B-A2B, a 24-billion parameter model that uses a hybrid architecture blending attention and convolution to address scaling bottlenecks in large language models. This innovation allows for high performance and efficiency on consumer-grade hardware with limited memory, making it possible to run complex AI locally without the need for data center infrastructure.

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Liquid AI's New LFM2-24B-A2B Hybrid Architecture Blends Attention with Convolutions to Solve the Scaling Bottlenecks of Modern LLMs

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