PSSA: A non-transformer language model written from scratch in Rust

This article describes a research prototype of a non-transformer language model built in Rust that reportedly learns faster than a same-size transformer and generates text about 12x faster on CPU, suggesting a potential path to lower infrastructure costs and better latency for inference-heavy AI workloads. For CIOs and technology leaders, the strategic signal is that alternatives to transformers may become viable for edge, on-prem, or cost-sensitive deployments, but the current results are still at small scale and should be treated as early-stage rather than production-ready. IT organizations should watch this class of recurrent/state-space architectures as a possible future option for reducing GPU dependence and broadening where AI can be deployed.