Tracel Technologies
Open-source AI infrastructure for portable, efficient model deployment.
| Date | Investors | Amount | Round |
|---|---|---|---|
* | $3.0m | Seed | |
| Total Funding | 000k | ||
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Tracel Technologies is a software and technology company tackling AI model portability and efficiency across different platforms. Founded in Quebec City, Canada, in 2023 by Nathaniel Simard (CEO) and Louis Fortier-Dubois, the company aims to simplify the development and deployment of artificial intelligence. Simard, who created the Burn deep learning framework, has a background in researching drug discovery models and holds a Master of Science from MILA in machine learning. Fortier-Dubois also holds a Master of Science from MILA and has over five years of experience with computer vision models in production.
The firm develops deep learning infrastructure optimized for both training and inference. Its core open-source product is Burn, a deep learning framework built with the Rust programming language, which emphasizes flexibility, computational efficiency, and portability. Tracel is also developing CubeCL, a multi-platform, high-performance compute language extension for Rust. These tools are designed to solve challenges such as the significant effort required to port models between platforms, hardware fragmentation, and the need for hardware-specific optimizations. By providing a unified foundation, Tracel enables researchers and engineers to build and deploy AI models more efficiently, reducing development time and operational costs.
The company's business model involves supporting businesses that build on its open-source framework by providing complementary commercial tools, custom training, and premium support. In August 2025, Tracel announced it had secured $3 million in a seed funding round to enhance its open-source tools and accelerate research and development. The company was also an incubatee at Quantino.
Keywords: AI model portability, deep learning framework, AI infrastructure, open-source AI, Rust programming language, machine learning operations, MLOps, model deployment, high-performance computing, AI efficiency, Burn framework, CubeCL, model training, model inference, compiler tools, AI developer tools, computational efficiency, cross-platform AI, AI scalability, embedded AI