Supersonic Labs Releases Julia 1: A 144.3M-Parameter Open Decision Model That Runs on a CPU

Supersonic Labs, a small AI lab from Brazil, released Julia 1, a 144.3M-parameter open decision model that runs on a plain CPU and is published on Hugging Face under Apache 2.0. Julia 1 starts from JHU CLSP's 140M-parameter mmBERT-small encoder and handles three decision types — choice, score, and noul — returning softmax probabilities in the caller's option order, with total cloud GPU training spend of about R$540 (US$104.08). In a September 24, 2026 evaluation on H200 BF16, Julia 1 scored 73.15% (1,463/2,000) on Typed Decisions versus a 72.70% Jev reference, 94/100 on AG News, 86/100 on DAIR Emotion, and 64/100 on Banking77 against an 87% reference, the lab's stated clear failure.

Supersonic Labs https://supersoniclabs.ia.br/ , a small AI lab from Brazil, has released Julia 1 https://supersoniclabs.ia.br/julia-1/ . It is a compact decision model, not a chatbot. You pass it context, a question, and 2 to 20 candidate answers. It picks one and returns a probability for every option. The model has 144.3M parameters and runs on a plain CPU. Is it deployable? Yes. The weights are on Hugging Face https://huggingface.co/SupersonicLabs/Julia-1 under Apache 2.0 and run locally with Python 3.11+ on CPU or a BF16-capable GPU. An ONNX build https://huggingface.co/SupersonicLabs/Julia-1-ONNX also runs in the browser via WebGPU. A hosted API is announced but not open yet. What Julia 1 Does Julia 1 handles three decision types through one API: - choice: pick one label from 2 to 20 described options classification, routing . - score: return the expected index on an ordered rubric, such as low, medium, high. - noul: return the probability that a yes-or-no statement is true. Results come back in the caller’s option order with full softmax probabilities. Caller IDs such as billing are returned unchanged. The model does not generate text. Architecture and Training Budget Julia 1 starts from JHU CLSP’s mmBERT-small https://huggingface.co/jhu-clsp/mmBERT-small , a 140M-parameter multilingual ModernBERT encoder trained on 1,800+ languages. Supersonic Labs kept the encoder and tokenizer, added a decision head, and trained on decision-format examples. The lab states Julia 1 is not a fine-tuned Qwen model . The runtime supports 8,192 combined tokens, but published benchmarks used a 1,024-token limit. Total cloud GPU spend for training and experiments was about R$540 US$104.08 . The FP32 weights occupy 550.5 MiB. The private training pipeline is not released. Julia 2, with the lab’s own foundation architecture, is in development. Benchmark Results The September 24, 2026 evaluation ran on H200 BF16 with strict encoding. The comparison baseline is TypeSafe’s Jev https://typesafe.ai/blog/introducing-system-one-models-and-jev , using reference values from the Jev benchmark protocol https://github.com/AbdelStark/jev-benchmarks , not a new Jev run. - Typed Decisions https://huggingface.co/datasets/LocalLLaMA/typed-decisions : 73.15% 1,463/2,000 vs 72.70% reference. - AG News, 4 labels: 94/100 vs 91% reference. - DAIR Emotion, 6 labels: 86/100 vs 48% reference. - Banking77, 72 labels: 64/100 vs 87% reference. This is the clear failure. - MASSIVE https://huggingface.co/datasets/AmazonScience/massive , 18 scenarios: 71.50% macro accuracy across 52 locales; 86.25% pt-PT, 86.75% en-US. The classification pilots use only 100 examples each. A September 25 CPU run https://supersoniclabs.ia.br/data/julia-1-cpu-20260925.json reproduced most numbers: 72.55% on Typed Decisions and 60/100 on Banking77 with 3 abstentions. On-Device Latency The lab published per-device measurements https://supersoniclabs.ia.br/data/julia-1-hardware-20260926.json . On an Apple M4, one decision per call took a 33.15 ms median. On a Samsung SM-X510 tablet via ONNX Runtime, the median was 203 ms with 393.1 MB peak RSS. On an Intel Core i5-1235U, AG News decisions took a 107.83 ms median. Banking77 took 3,713.54 ms because it narrows 72 labels first. On X, @supersonicai https://x.com/supersonicai claims Julia 1 classifies 5x faster than Jev on an i5 laptop. Treat that carefully. The Jev pilot measured Jev as a hosted service called from France, so latencies are not like-for-like. Interactive Explainer Julia 1 vs Closest Competitors | Feature | Julia 1 | TypeSafe Jev | GLiNER2.5 Multi | |---|---|---|---| | Developer | Supersonic Labs | TypeSafe AI | Fastino | | Access | Open weights | Hosted API, early access | Open weights | | License | Apache 2.0 | Proprietary | Apache 2.0 | | Parameters | 144.3M | Not disclosed | 287M | | Base encoder | mmBERT-small | Not disclosed | mDeBERTa-v3-base | | Decision types | Choice, score, yes/no | Typed structured decisions | Classification, NER, relations, records | | Options per call | 2 to 20 Router for more | Up to 255 | Label list per schema | | Runs locally on CPU | Yes | No | Yes | | Input price per 1M tokens | $0.025 planned API | $0.042 | Free self-hosted | | AG News pilot | 94% | 91% | 70% | | DAIR Emotion pilot | 86% | 48% | 44% | | Banking77 pilot | 64% | 87% | 61% | Sources: Julia 1 model card https://huggingface.co/SupersonicLabs/Julia-1 , TypeSafe launch post https://typesafe.ai/blog/introducing-system-one-models-and-jev , GLiNER2.5 Multi card https://huggingface.co/fastino/gliner2.5-multi-v1 , Jev benchmark pilot https://github.com/AbdelStark/jev-benchmarks . Julia 1 pilots ran separately from the Jev and GLiNER runs. Limitations Julia 1 compares the answers you supply. It cannot be counted on for missing facts, algebra, or multi-step calculation. The Router can drop the correct label during narrowing. It is not a drop-in Transformers pipeline, and no Hugging Face inference provider serves it. Supersonic Labs advises evaluating on your own questions and keeping humans in the loop for consequential decisions. Key Takeaways - Julia 1 is a 144.3M-parameter, Apache 2.0 decision model that runs on CPU. - One API covers choice, ordered score, and yes-or-no decisions over 2 to 20 options. - It beat Jev references on 3 of 4 pilots but trailed badly on 72-label Banking77. - Median latency hit 33.15 ms per decision on an Apple M4. - Training cost about US$104 in cloud GPUs; a $0.025/MTok API is planned. Check out the Model Weights https://huggingface.co/SupersonicLabs/Julia-1 , ONNX/WebGPU build https://huggingface.co/SupersonicLabs/Julia-1-ONNX , and Technical details https://supersoniclabs.ia.br/julia-1/ . All credit goes to the researcher of this project. Also, feel free to follow us on Twitter https://x.com/intent/follow?screen name=marktechpost and don’t forget to join our 150k+ML SubReddit https://www.reddit.com/r/machinelearningnews/ and Subscribe to our Newsletter https://magic.beehiiv.com/v1/f5e63dd4-5653-4f09-83e2-321a8b1ba526?email={{email}} . Wait are you on telegram? now you can join us on telegram as well. https://t.me/machinelearningresearchnews Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us https://forms.gle/MJjjVDPS7whH8Ngs6 Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. 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