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huggingface/candle

📈 Trending★ 20,292RustAudience · developerComplexity · 3/5ActiveLicenseSetup · moderate

TLDR

A fast, lightweight machine learning framework written in Rust that lets you run AI models directly without Python, with GPU support and pre-built implementations of popular models.

Mindmap

mindmap
  root((Candle))
    What it does
      Run AI models in Rust
      GPU acceleration support
      Minimal and fast design
    Models included
      Text generators
      Image generators
      Speech recognition
      Object detection
    Tech stack
      Rust language
      WebAssembly support
      GPU compute
    Use cases
      Production AI apps
      Edge device inference
      Browser-based AI
      Rust applications

Things people build with this

USE CASE 1

Build a Rust application that runs LLaMA or Mistral text models without calling Python.

USE CASE 2

Deploy Stable Diffusion image generation on edge devices or servers where speed is critical.

USE CASE 3

Run speech recognition or object detection in a web browser using WebAssembly.

USE CASE 4

Create a production AI service in Rust that processes tensors on GPU for low-latency inference.

Tech stack

RustGPU/CUDAWebAssemblyTensors

Getting it running

Difficulty · moderate Time to first run · 30min

GPU/CUDA setup required for full performance; CPU-only fallback available but slower.

Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.

In plain English

Candle is a machine learning framework built in Rust, a programming language known for being fast and memory-efficient. Made by Hugging Face, the company behind many popular AI tools, Candle lets developers run AI models directly in Rust code rather than relying on Python, which is the more common choice for AI work. The core idea is to keep things minimal and fast. Instead of a heavy library full of features you might never use, Candle focuses on performance. It supports running computations on a GPU (a graphics card repurposed for heavy math tasks), which dramatically speeds up AI processing. The code examples in the README show how straightforward it is: you create tensors (grids of numbers that AI models work with), do math on them, and optionally shift computation from CPU to GPU by changing a single line. Candle comes with ready-to-run implementations of many well-known AI models, including text generators like LLaMA, Mistral, Gemma, and Phi, image generators like Stable Diffusion, speech recognition via Whisper, object detection via YOLO, image captioning via BLIP, and many others. Some of these even run in a web browser via WebAssembly, meaning the AI runs on the user's own device, not on a server. You would use Candle if you are building an AI-powered Rust application and want a lightweight, performant foundation instead of wrapping Python libraries. It is particularly useful for production deployments where speed matters, or for running models on edge devices. The project is written in Rust and published on crates.io, Rust's package registry.

Copy-paste prompts

Prompt 1
Show me how to load and run a LLaMA model in Candle with GPU acceleration, step by step.
Prompt 2
How do I convert a PyTorch model to run in Candle? Give me a concrete example.
Prompt 3
Create a Rust CLI tool that uses Candle to generate images with Stable Diffusion.
Prompt 4
What's the simplest way to run Whisper speech recognition in Candle? Show me working code.
Prompt 5
How do I deploy a Candle model to run in a web browser using WebAssembly?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.