Tracel

The unified AI stack built on your compute

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Run models on the hardware of your choice for both training and inference.

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Mission

Tracel

We are on a mission to redefine AI software infrastructure to put the world's compute to full use.
We think wasted compute is wasted intelligence.

Done right, intelligence can help cure diseases, lift people out of poverty and make abundance the norm. All of that takes intelligence at an enormous scale.

Compute is the raw material of intelligence. Our contribution is making sure none of it goes to waste.

Join us in our mission

Tracel bridges the gapbetween silicon and models

Metabolic

An AI engine to deploy any open-weight model.

Burn

A deep learning framework to train and run models efficiently on any device.

CubeCL

A Rust language extension to write high-performance compute for any platform.

Our compute ecosystem

GitHub/tracel-ai15K+ stars on GitHub

A deep learning framework to train and run models efficiently on any device.

Train and run neural networks on any hardware, from one codebase.

Burn handles dynamic graphs and shapes with the performance of static graphs, thanks to its multiplatform just-in-time compiler.

A Rust language extension to write high-performance compute for any platform.

Write a kernel once as a #[cube] Rust function and run it on CUDA, HIP, Metal, SPIR-V, WGSL or CPU SIMD.

CubeCL's just-in-time compiler uses the best instructions each platform offers, so a single kernel can reach peak performance on every backend.

An AI engine to deploy any open-weight model.

Pick a pre-optimized open-weight model and get it running: download it, or embed it straight into your code.

Then make it yours: speed it up further on your own hardware, and soon, adapt it to your own data.

Ready for the future

On-device personalization

Models that adapt to each user, right on their device.

Federated learning

Models trained across thousands of devices, without moving raw data.

Sovereign by default

Models trained and served on your own infrastructure.

Continual learning

Models that keep learning after they ship.

Adaptive edge & robotics

Agents that learn in the physical world they operate in.

Interleaved workloads

The same machines switch between training and inference as demand changes.

From computeto intelligence

Optimized down to every chip

Managed across every deployment

The unified AI stack

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Control over every layer

A full-stack ecosystem, from GPU kernels to model deployment, largely open source.

Explore our technologies

Shape the future of AI with us