burn

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Burn is both a tensor library and a deep learning framework, optimized for numerical computing, training and inference.

Training and inference usually live in separate worlds. Models are typically trained in Python then exported to an open format like ONNX or optimized for production engines like vLLM, ONNX Runtime, or TensorRT. This export step is often brittle and lossy, ruling out complex architectures and advanced deployment use cases.

Burn unifies the two. By executing multi-platform tensor operations via a single, unified API, the exact code used for training is the exact code that runs in production. This makes workloads like on-device personalization and federated learning straightforward, while enabling teams to go from prototype to deployment in a single codebase.

Burn preserves the intuitive ergonomics of PyTorch, with dynamic shapes and graphs, but JIT-compiles streams of tensor operations, performing automatic kernel fusion. You get the flexibility of dynamic graphs without the performance drop.

Rust for Research?

Rust used to be a tough sell for research: long compilation times disrupted the fast edit-compile-run loop that draws researchers to Python. Burn changes this paradigm. Designed around incremental compilation, modifying model code recompiles in under 5 seconds, even in release mode. This delivers a Python-like feedback loop with the speed and safety of Rust.

Ecosystem

Burn is the core of a growing, fully open-source Rust AI ecosystem. You are not adopting a single library, you are joining a stack that spans GPU compute, model interop and domain toolkits, with plenty of room to help shape what comes next.

Category	Project	Description
Compute	CubeCL	GPU compute language and compiler behind Burn's accelerated backends. Write kernels once in Rust, run on CUDA, ROCm, Metal, Vulkan and WebGPU. Usable standalone.
Model interop	burn-onnx	Import ONNX models into Burn as native Rust code
	burn-store	Save, load and import model weights, including PyTorch and Safetensors
Domains	burn-vision	Computer vision operators and building blocks
	burn-rl	Reinforcement learning building blocks
	burn-dataset	Dataset loading, transforms and ready-made sources
Models	models	Curated pre-trained models and examples built with Burn
Tooling	burn-bench	Benchmark and compare backends, tracking performance over time

Burn's CubeCL backends (CUDA, ROCm, Metal, Vulkan, WebGPU, CPU) compose with autodiff, fusion and remote-execution decorators, while external and simpler backends (LibTorch and pure-Rust CPU/no_std) compose with autodiff only. See Supported Backends below for the full matrix.

Every project here is open-source and actively developed. Want to help build the Rust AI ecosystem? The good first issues are a great place to start, and the Contributing guide will get you set up.

These crates are not maintained by Tracel, but they are part of the same Rust AI story. Anything that helps you load data, build environments, or ship models belongs here. Built something that fits? Open a PR to add it!

Category	Crate	Description
Data & loading	polars	Fast DataFrames for tabular data
	arrow-rs	Apache Arrow columnar memory format
	image	Image decoding, encoding and processing
	hf-hub	Download models and datasets from the Hugging Face Hub
Tokenization & NLP	tokenizers	Fast, production-ready tokenizers
	rust-bert	Ready-to-use NLP pipelines and transformer models
Numerical & linear algebra	ndarray	N-dimensional arrays
	nalgebra	Linear algebra
Classical ML	linfa	Classical ML toolkit, in the spirit of scikit-learn
	smartcore	Classical ML algorithms, no BLAS/LAPACK required
Inference & runtimes	candle	Minimalist ML framework with a focus on LLM inference
	mistral.rs	Fast, multimodal LLM inference engine
	ort	ONNX Runtime bindings for hardware-accelerated inference
	tract

Backend

Burn strives to be as fast as possible on as many hardwares as possible, with robust implementations. We believe this flexibility is crucial for modern needs where you may train your models in the cloud, then deploy on customer hardwares, which vary from user to user.

Supported Backends

Most backends support all operating systems, so we don't mention them in the tables below.

GPU Backends:

	CUDA	ROCm	Metal	Vulkan	WebGPU	LibTorch
Nvidia	☑️	-	-	☑️	☑️	☑️
AMD	-	☑️	-	☑️	☑️	☑️
Apple	-	-	☑️	-	☑️	☑️
Intel	-	-	-	☑️	☑️	-
Qualcom	-	-	-	☑️	☑️	-
Wasm	-	-	-	-	☑️	-

CPU Backends:

	Cpu (CubeCL)	Flex	LibTorch
X86	☑️	☑️	☑️
Arm	☑️	☑️	☑️
Wasm	-	☑️	-
no-std	-	☑️	-

Compared to other frameworks, Burn has a very different approach to supporting many backends. By design, most code is generic over the Backend trait, which allows us to build Burn with swappable backen