PSSA, a 2.7-billion-parameter language model built entirely in Rust, replaces transformer self-attention with a recurrent-convolutional architecture that processes tokens in linear time, avoiding quadratic memory scaling. Released under Apache 2.0, it achieved comparable performance to GPT-3.5 on standard benchmarks while excelling at long-context tasks up to 32k tokens, and runs 1.8× faster with 30% less RAM than PyTorch transformers.