Today we release Needle 2: an open 45M-parameter model for tool calling, device use and structured extraction. The whole model is a single 14MB binary that runs a full session in 28MB of RAM. It is built on our Simple Attention Network findings, compressed to CQ2-bit with Cactus Quants, and baked into its own engine. On the tool call and mobile device use benchmarks, Needle 2 trades wins with other small models like FunctionGemma 270M, LFM2.5 230M and Apple FM, at 5× to 70× smaller, and 2 bits against their f16. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, between 400–1,500 tok