Software Alternatives, Accelerators & Startups

CUDA Toolkit VS MC Stan

Compare CUDA Toolkit VS MC Stan and see what are their differences

CUDA Toolkit logo CUDA Toolkit

Select Target Platform Click on the green buttons that describe your target platform.

MC Stan logo MC Stan

Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences.
  • CUDA Toolkit Landing page
    Landing page //
    2024-05-30
  • MC Stan Landing page
    Landing page //
    2023-08-18

CUDA Toolkit videos

1971 Plymouth Cuda 440: Regular Car Reviews

More videos:

  • Review - Jackson Kayak Cuda Review
  • Review - Great First Effort! The New $249 Signum Cuda

MC Stan videos

MC STΔN NUMBERKARI REACTION | MC STAN NUMBERKARI REACTION | MC STAN NEW SONG | TADIPAAR 2K20 | AFAIK

More videos:

  • Review - What is MC STAN ? Is he really worth all the hype? TADIPAAR ALBUM REVIEW | Desi Hip-Hop
  • Review - MC STΔN AMIN REACTION | AMIN REACTION | MC STAN AMIN REACTION | MC STAN REACTION | TADIPAAR | AFAIK

Category Popularity

0-100% (relative to CUDA Toolkit and MC Stan)
Data Science And Machine Learning
Application Utilities
100 100%
0% 0
Data Science
0 0%
100% 100
Machine Learning Tools
100 100%
0% 0

User comments

Share your experience with using CUDA Toolkit and MC Stan. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, CUDA Toolkit should be more popular than MC Stan. It has been mentiond 37 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

CUDA Toolkit mentions (37)

  • Deploying llama.cpp on AWS (with Troubleshooting)
    Install CUDA Toolkit (only the Base Installer). Download it and follow instructions from Https://developer.nvidia.com/cuda-downloads. - Source: dev.to / 19 days ago
  • A comprehensive guide to running Llama 2 locally
    For my fellow Windows shills, here's how you actually build it on windows: Before steps: 1. (For Nvidia GPU users) Install cuda toolkit https://developer.nvidia.com/cuda-downloads 2. Download the model somewhere: https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/resolve/main/llama-2-13b-chat.ggmlv3.q4_0.bin In Windows Terminal with Powershell:
        git clone https://github.com/ggerganov/llama.cpp.
    - Source: Hacker News / 11 months ago
  • Nvidia with linux....... not a good combination
    I use Ubuntu and configuring nvidia drivers is very easy installing from here https://developer.nvidia.com/cuda-downloads. Source: 12 months ago
  • Can't get CLBLAST working on oobabooga
    You have posted almost no information about your Hardware and what exactly you have done. Do you actually have NVIDIA? Have you actually installed CUDA? Also when exactly do you get the error, while installed the python package or later? Source: 12 months ago
  • NEW NVIDIA 535.98 DRIVER!!- INCREASE SPEED, POWER, IMAGE SIZE AN WHO KNOW WHAT ELSE MORE!
    EDIT: LINK TO CUDA-toolkit: https://developer.nvidia.com/cuda-downloads. Source: about 1 year ago
View more

MC Stan mentions (24)

  • [Q] Is there a method for adding random effects to an interval censored time to event model?
    My approach to problems like this is to write down the proposed model mathematically first, in extreme detail. I find hierarchical form to be the easiest way to break it down piece by piece. Once I have the maths then I turn it into a Stan model. Last step is to use the Stan output to answer the research questions. Source: about 1 year ago
  • Demand Planning
    For instance my first choice in these cases is always a Bayesian inference tool like Stan. In my experience as someone who’s more of a programmer than mathematician/statistician, Bayesian tools like this make it much easier to not accidentally fool yourself with assumptions, and they can be pretty good at catching statistical mistakes. Source: about 1 year ago
  • What do actual ML engineers think of ChatGPT?
    I tend to be most impressed by tools and libraries. The stuff that has most impressed me in my time in ML is stuff like pytorch and Stan, tools that allow expression of a wide variety of statistical (and ML, DL models, if you believe there's a distinction) models and inference from those models. These are the things that have had the largest effect in my own work, not in the sense of just using these tools, but... Source: about 1 year ago
  • How to get started learning modern AI?
    Oh its certainly used in practice. You should look into frameworks like Stan[1] and pyro[2]. I think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing [1] https://mc-stan.org/. - Source: Hacker News / about 1 year ago
  • Should I start learning R, SAS, or Python during my gap year?
    At this point the only people using such things are the programmers. Think e.g. STAN. https://mc-stan.org/ the rest of us: R, SAS, Excel. Source: over 1 year ago
View more

What are some alternatives?

When comparing CUDA Toolkit and MC Stan, you can also consider the following products

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

MLKit - MLKit is a simple machine learning framework written in Swift.

Gensim - Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora.