Supersonic Labs releases Julia 1, a 144.3 million-parameter open-source decision model that runs on CPUs

Brazilian AI lab Supersonic Labs has released Julia 1, an open-source decision model built for structured decision tasks rather than general-purpose language generation. The model has 144.3 million parameters and is designed to run locally on standard CPUs, removing the need for high-end GPUs. Its weights have been published on Hugging Face under the Apache 2.0 license, and the model also supports browser-side execution through ONNX with WebGPU. Julia 1 uses a single API to handle three types of decision workflows: choosing from 2 to 20 options, assigning ratings on ordered scales such as low, medium, and high, and estimating the probability that a yes-or-no statement is true. Supersonic Labs said the model is built on Johns Hopkins University’s mmBERT-small encoder and is not a fine-tuned version of an existing large language model. The lab put total training cost at about $104. In benchmark results released by the team, Julia 1 posted 73.15% accuracy on the Typed Decisions task, slightly above the reference baseline for the TypeSafe Jev model. Performance was weaker on some other tasks, including Banking77, where accuracy reached 64% across a 72-label classification setting. On Apple’s M4 chip, the median latency for a single decision was 33.15 milliseconds.