# Supersonic Labs Releases Julia 1: A 144.3M-Parameter Open Decision Model That Runs on a CPU > Source: > Published: 2026-09-26 19:50:57+00:00 [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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