Liquid AI Releases Fast Bidirectional Encoders LFM2.5-Encoder at 8K Context on CPU

Liquid AI has released two new bidirectional encoder models — LFM2.5-Encoder-230M and LFM2.5-Encoder-350M — designed to maintain fast inference at 8K context length while running on CPU hardware. Bidirectional encoders are the backbone of embedding-heavy workloads like semantic search, retrieval-augmented generation, classification, and reranking, and the ability to run 8K context efficiently on CPU is a meaningful constraint lift for cost-sensitive deployments. Liquid AI's LFM architecture continues to differentiate itself from transformer-based alternatives, and these encoder models extend the family into a new and highly practical use case. Developers building RAG pipelines or similarity search systems who want to avoid GPU dependency for the retrieval layer should evaluate these models directly. The 230M and 350M parameter sizes also keep memory footprint manageable for edge or embedded deployment scenarios.