GPU & Compute

Vulpes

Deep learning framework in F# with CUDA support for GPU execution.

Status
UnmaintainedGitHub repository has been archived.
Last checked
Checked · Oct 13, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

Vulpes is a deep belief and deep learning implementation written in F#, leveraging CUDA GPU execution through Alea.cuBase, making it suitable for high-performance computing tasks.

Vulpes stands out by providing deep learning capabilities in F# with native support for CUDA-accelerated GPU execution, making it ideal for high-performance computing tasks that require both precision and speed.

When it fits — and when it doesn’t

A good fit for

  • Developers working with F# who need a deep learning framework that supports GPU acceleration via CUDA.

  • Research teams focusing on neural networks and machine learning algorithms in an academic or industrial setting.

Look elsewhere if

  • Projects requiring real-time processing where latency is critical, as setup might be more complex compared to cloud-based services.

  • Teams looking for a fully managed service without the need to handle GPU configurations and setups.

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Steep learning curve for non-F# developers

    Vulpes is implemented in F#, which may be unfamiliar to many developers accustomed to more mainstream languages like Python or Java.

  2. 02

    Limited community support and documentation

    As an open-source project with a niche language focus, Vulpes has a relatively small user base which can result in fewer resources for troubleshooting and learning.

  3. 03

    Dependence on Alea.cuBase for CUDA integration introduces additional complexity

    Integrating with Alea.cuBase requires understanding both F# and the specific CUDA API, which can be challenging and may lead to increased development time.

  4. 04

    Potential performance overhead due to language-specific limitations

    While Vulpes leverages CUDA for GPU execution, the performance might still be constrained by F# runtime characteristics or interoperability issues with Alea.cuBase.

What it does well

  • Deep belief and deep learning implementation in F#

  • Leverages CUDA for GPU execution with Alea.cuBase

  • Open-source under MIT license

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

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Set up Vulpes

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →
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