candle

Candle is a minimalist ML framework for Rust with a focus on performance (including GPU support) and ease of use. Try our online demos: whisper, LLaMA2, T5, yolo, Segment Anything.

Get started

Make sure that you have candle-core correctly installed as described in Installation.

Let’s see how to run a simple matrix multiplication. Write the following to your myapp/src/main.rs file:

use candle_core::{Device, Tensor};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let device = Device::Cpu;

    let a = Tensor::randn(0f32, 1., (2, 3), &device)?;
    let b = Tensor::randn(0f32, 1., (3, 4), &device)?;

    let c = a.matmul(&b)?;
    println!("{c}");
    Ok(())
}

cargo run should display a tensor of shape Tensor[[2, 4], f32].

Having installed candle with Cuda support, simply define the device to be on GPU:

- let device = Device::Cpu;
+ let device = Device::new_cuda(0)?;

For more advanced examples, please have a look at the following section.

Check out our examples

These online demos run entirely in your browser:

yolo: pose estimation and object recognition.
whisper: speech recognition.
LLaMA2: text generation.
T5: text generation.
Phi-1.5, and Phi-2: text generation.
Segment Anything Model: Image segmentation.
BLIP: image captioning.

We also provide some command line based examples using state of the art models:

LLaMA v1, v2, and v3: general LLM, includes the SOLAR-10.7B variant.
Falcon: general LLM.
Codegeex4: Code completion, code interpreter, web search, function calling, repository-level
GLM4: Open Multilingual Multimodal Chat LMs by THUDM
Gemma v1 and v2: 2b and 7b+/9b general LLMs from Google Deepmind.
RecurrentGemma: 2b and 7b Griffin based models from Google that mix attention with a RNN like state.
Phi-1, Phi-1.5, Phi-2, and Phi-3: 1.3b, 2.7b, and 3.8b general LLMs with performance on par with 7b models.
StableLM-3B-4E1T: a 3b general LLM pre-trained on 1T tokens of English and code datasets. Also supports StableLM-2, a 1.6b LLM trained on 2T tokens, as well as the code variants.
Mamba: an inference only implementation of the Mamba state space model.
Mistral7b-v0.1: a 7b general LLM with better performance than all publicly available 13b models as of 2023-09-28.
Mixtral8x7b-v0.1: a sparse mixture of experts 8x7b general LLM with better performance than a Llama 2 70B model with much faster inference.
StarCoder and StarCoder2: LLM specialized to code generation.
Qwen1.5: Bilingual (English/Chinese) LLMs.
RWKV v5 and v6: An RNN with transformer level LLM performance.
Replit-code-v1.5: a 3.3b LLM specialized for code completion.
Yi-6B / Yi-34B: two bilingual (English/Chinese) general LLMs with 6b and 34b parameters.
Quantized LLaMA: quantized version of the LLaMA model using the same quantization techniques as llama.cpp.
Quantized Qwen3 MoE: support gguf quantized models of Qwen3 MoE models.

Stable Diffusion: text to image generative model, support for the 1.5, 2.1, SDXL 1.0 and Turbo versions.

Wuerstchen: another text to image generative model.

yolo-v3 and yolo-v8: object detection and pose estimation models.

segment-anything: image segmentation model with prompt.

SegFormer: transformer based semantic segmentation model.
Whisper: speech recognition model.
EnCodec: high-quality audio compression model using residual vector quantization.
MetaVoice: foundational model for text-to-speech.
Parler-TTS: large text-to-speech model.
T5, Bert, JinaBert : useful for sentence embeddings.
DINOv2: computer vision model trained using self-supervision (can be used for imagenet classification, depth evaluation, segmentation).
VGG, RepVGG: computer vision models.
BLIP: image to text model, can be used to generate captions for an image.
CLIP: multi-model vision and language model.
TrOCR: a transformer OCR model, with dedicated submodels for hand-writing and printed recognition.
Marian-MT: neural machine translation model, generates the translated text from the input text.
Moondream: tiny computer-vision model that can answer real-world questions about images.

Run them using commands like:

cargo run --example quantized --release

In order to use CUDA add --features cuda to the example command line. If you have cuDNN installed, use --features cudnn for even more speedups. The opt-in cutile feature supports writing JIT-compiled CUDA kernels in Rust; see the cuTile guide for setup and interop details.

There are also some wasm examples for whisper and llama2.c. You can either build them with trunk or try them online: whisper, llama2, T5, Phi-1.5, and Phi-2, Segment Anything Model.

For LLaMA2, run the following command to retrieve the weight files and start a test server:

# install target platform 'wasm32-unknown-unknown'
rustup target add wasm32-unknown-unknown

cd candle-wasm-examples/llama2-c
wget https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/model.bin
wget https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/tokenizer.json
trunk serve --release --port 8081

And then head over to http://localhost:8081/.

Useful External Resources

candle-tutorial: A very detailed tutorial showing how to convert a PyTorch model to Candle.
candle-lora: Efficient and ergonomic LoRA implementation for Candle. candle-lora has
out-of-the-box LoRA support for many models from Candle, which can be found here.
candle-video: Rust library for text-to-video generation (LTX-Video and related models) built on Candle, focused on fast, Python-free inference.
optimisers: A collection of optimisers including SGD with momentum, AdaGrad, AdaDelta, AdaMax, NAdam, RAdam, and RMSprop.
candle-vllm: Efficient platform for inference and serving local LLMs including an OpenAI compatible API server.
candle-ext: An extension library to Candle that provides PyTorch functions not currently available in Candle.
candle-coursera-ml: Implementation of ML algorithms from Coursera’s Machine Learning Specialization course.
kalosm: A multi-modal meta-framework in Rust for interfacing with local pre-trained models with support for controlled generation, custom samplers, in-memory vector databases, audio transcription, and more.
candle-sampling: Sampling techniques for Candle.
gpt-from-scratch-rs: A port of Andrej Karpathy’s Let’s build GPT tutorial on YouTube showcasing the Candle API on a toy problem.
candle-einops: A pure rust implementation of the python einops library.
atoma-infer: A Rust library for fast inference at scale, leveraging FlashAttention2 for efficient attention computation, PagedAttention for efficient KV-cache memory management, and multi-GPU support. It is OpenAI api compatible.
llms-from-scratch-rs: A comprehensive Rust translation of the code from Sebastian Raschka’s Build an LLM from Scratch book.
vllm.rs: A minimalist vLLM implementation in Rust based on Candle.

If you have an addition to this list, please submit a pull request.

Features

Simple syntax, looks and feels like PyTorch.
- Model training.
- Embed user-defined ops/kernels, such as flash-attention v2.
Backends.
- Optimized CPU backend with optional MKL support for x86 and Accelerate for macs.
- CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL.
- WASM support, run your models in a browser.
Included models.
- Language Models.
  - LLaMA v1, v2, and v3 with variants such as SOLAR-10.7B.
  - Falcon.
  - StarCoder, StarCoder2.
  - Phi 1, 1.5, 2, and 3.
  - Mamba, Minimal Mamba
  - Gemma v1 2b and 7b+, v2 2b and 9b.
  - Mistral 7b v0.1.
  - Mixtral 8x7b v0.1.
  - StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B.
  - Replit-code-v1.5-3B.
  - Bert.
  - Yi-6B and Yi-34B.
  - Qwen1.5, Qwen1.5 MoE, Qwen3 MoE.
  - RWKV v5 and v6.
- Quantized LLMs.
  - Llama 7b, 13b, 70b, as well as the chat and code variants.
  - Mistral 7b, and 7b instruct.
  - Mixtral 8x7b.
  - Zephyr 7b a and b (Mistral-7b based).
  - OpenChat 3.5 (Mistral-7b based).
  - Qwen3 MoE (16B-A3B, 32B-A3B)
- Text to text.
  - T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
  - Marian MT (Machine Translation).
- Text to image.
  - Stable Diffusion v1.5, v2.1, XL v1.0.
  - Wurstchen v2.
- Image to text.
  - BLIP.
  - TrOCR.
- Audio.
  - Whisper, multi-lingual speech-to-text.
  - EnCodec, audio compression model.
  - MetaVoice-1B, text-to-speech model.
  - Parler-TTS, text-to-speech model.
- Computer Vision Models.
  - DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT, ConvNeXTv2, MobileOne, EfficientVit (MSRA), MobileNetv4, Hiera, FastViT.
  - yolo-v3, yolo-v8.
  - Segment-Anything Model (SAM).
  - SegFormer.
File formats: load models from safetensors, npz, ggml, or PyTorch files.
Serverless (on CPU), small and fast deployments.
Quantization support using the llama.cpp quantized types.

How to use

Cheatsheet:

	Using PyTorch	Using Candle
Creation	`torch.Tensor([[1, 2], [3, 4]])`	`Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)?`
Creation	`torch.zeros((2, 2))`	`Tensor::zeros((2, 2), DType::F32, &Device::Cpu)?`
Indexing	`tensor[:, :4]`	`tensor.i((.., ..4))?`
Operations	`tensor.view((2, 2))`	`tensor.reshape((2, 2))?`
Operations	`a.matmul(b)`	`a.matmul(&b)?`
Arithmetic	`a + b`	`&a + &b`
Device	`tensor.to(device="cuda")`	`tensor.to_device(&Device::new_cuda(0)?)?`
Dtype	`tensor.to(dtype=torch.float16)`	`tensor.to_dtype(&DType::F16)?`
Saving	`torch.save({"A": A}, "model.bin")`	`candle::safetensors::save(&HashMap::from([("A", A)]), "model.safetensors")?`
Loading	`weights = torch.load("model.bin")`	`candle::safetensors::load("model.safetensors", &device)`

Structure

candle-core: Core ops, devices, and Tensor struct definition
candle-nn: Tools to build real models
candle-examples: Examples of using the library in realistic settings
candle-kernels: CUDA custom kernels
candle-datasets: Datasets and data loaders.
candle-transformers: transformers-related utilities.
candle-flash-attn: Flash attention v2 layer.
candle-onnx: ONNX model evaluation.

FAQ

Why should I use Candle?

Candle’s core goal is to make serverless inference possible. Full machine learning frameworks like PyTorch are very large, which makes creating instances on a cluster slow. Candle allows deployment of lightweight binaries.

Secondly, Candle lets you remove Python from production workloads. Python overhead can seriously hurt performance, and the GIL is a notorious source of headaches.

Finally, Rust is cool! A lot of the HF ecosystem already has Rust crates, like safetensors and tokenizers.

Other ML frameworks

dfdx is a formidable crate, with shapes being included in types. This prevents a lot of headaches by getting the compiler to complain about shape mismatches right off the bat. However, we found that some features still require nightly, and writing code can be a bit daunting for non rust experts.

We’re leveraging and contributing to other core crates for the runtime so hopefully both crates can benefit from each other.
burn is a general crate that can leverage multiple backends so you can choose the best engine for your workload.
tch-rs Bindings to the torch library in Rust. Extremely versatile, but they bring in the entire torch library into the runtime. The main contributor of tch-rs is also involved in the development of candle.

Common Errors

Missing symbols when compiling with the mkl feature.

If you get some missing symbols when compiling binaries/tests using the mkl or accelerate features, e.g. for mkl you get:

  = note: /usr/bin/ld: (....o): in function `blas::sgemm':
          .../blas-0.22.0/src/lib.rs:1944: undefined reference to `sgemm_' collect2: error: ld returned 1 exit status

  = note: some `extern` functions couldn't be found; some native libraries may need to be installed or have their path specified
  = note: use the `-l` flag to specify native libraries to link
  = note: use the `cargo:rustc-link-lib` directive to specify the native libraries to link with Cargo

or for accelerate:

Undefined symbols for architecture arm64:
            "_dgemm_", referenced from:
                candle_core::accelerate::dgemm::h1b71a038552bcabe in libcandle_core...
            "_sgemm_", referenced from:
                candle_core::accelerate::sgemm::h2cf21c592cba3c47 in libcandle_core...
          ld: symbol(s) not found for architecture arm64

This is likely due to a missing linker flag that was needed to enable the mkl library. You can try adding the following for mkl at the top of your binary:

extern crate intel_mkl_src;

or for accelerate:

extern crate accelerate_src;

Error: request error: https://huggingface.co/meta-llama/Llama-2-7b-hf/resolve/main/tokenizer.json: status code 401

This is likely because you’re not permissioned for the LLaMA-v2 model. To fix this, you have to register on the huggingface-hub, accept the LLaMA-v2 model conditions, and set up your authentication token. See issue #350 for more details.

Docker build

When building CUDA kernels inside a Dockerfile, nvidia-smi cannot be used to auto-detect compute capability.

You must explicitly set CUDA_COMPUTE_CAP, for example:

FROM nvidia/cuda:12.9.0-devel-ubuntu22.04

# Install git and curl
RUN set -eux; \
  apt-get update; \
  apt-get install -y curl git ca-certificates;

# Install Rust
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y

# Clone candle repo
RUN git clone https://github.com/huggingface/candle.git

# Set compute capability for the build
ARG CUDA_COMPUTE_CAP=90
ENV CUDA_COMPUTE_CAP=${CUDA_COMPUTE_CAP}

# Build with explicit compute cap
WORKDIR /app
COPY . .
RUN cargo build --release features cuda

Compiling with flash-attention fails

/usr/include/c++/11/bits/std_function.h:530:146: error: parameter packs not expanded with ‘...’:

This is a bug in gcc-11 triggered by the Cuda compiler. To fix this, install a different, supported gcc version - for example gcc-10, and specify the path to the compiler in the NVCC_CCBIN environment variable.

env NVCC_CCBIN=/usr/lib/gcc/x86_64-linux-gnu/10 cargo ...

Linking error on windows when running rustdoc or mdbook tests

Couldn't compile the test.
---- .\candle-book\src\inference\hub.md - Using_the_hub::Using_in_a_real_model_ (line 50) stdout ----
error: linking with `link.exe` failed: exit code: 1181
//very long chain of linking
 = note: LINK : fatal error LNK1181: cannot open input file 'windows.0.48.5.lib'

Make sure you link all native libraries that might be located outside a project target, e.g., to run mdbook tests, you should run:

mdbook test candle-book -L .\target\debug\deps\ `
-L native=$env:USERPROFILE\.cargo\registry\src\index.crates.io-6f17d22bba15001f\windows_x86_64_msvc-0.42.2\lib `
-L native=$env:USERPROFILE\.cargo\registry\src\index.crates.io-6f17d22bba15001f\windows_x86_64_msvc-0.48.5\lib

Extremely slow model load time with WSL

This may be caused by the models being loaded from /mnt/c, more details on stackoverflow.

Tracking down errors

You can set RUST_BACKTRACE=1 to be provided with backtraces when a candle error is generated.

CudaRC error

If you encounter an error like this one called Result::unwrap()on anErr value: LoadLibraryExW { source: Os { code: 126, kind: Uncategorized, message: "The specified module could not be found." } } on windows. To fix copy and rename these 3 files (make sure they are in path). The paths depend on your cuda version. c:\Windows\System32\nvcuda.dll -> cuda.dll c:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin\cublas64_12.dll -> cublas.dll c:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin\curand64_10.dll -> curand.dll