cunn
Torch CUDA Neural Network Implementation for GPU-accelerated deep learning.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is cunn?
cunn is a library that provides GPU acceleration for neural networks using CUDA, enabling faster training and inference in Torch. It's essential for developers working with large datasets or complex models who require high performance.
Key differentiator
“cunn stands out as a specialized library within the Torch ecosystem, offering direct CUDA integration to accelerate neural network operations on GPUs, making it ideal for performance-critical applications.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Lua is less popular compared to Python or JavaScript, limiting the developer community and available resources.
Setting up CUDA can be challenging due to hardware and driver compatibility issues which may require significant effort to resolve.
As an open-source project primarily in Lua, cunn might not have a large or active developer community for support and contributions.
cunn relies heavily on CUDA which is optimized for NVIDIA GPUs. Performance on other GPU brands may be suboptimal or unsupported.
Fit analysis
Who is it for?
✓ Best for
Teams working with large datasets and complex neural network architectures who need GPU acceleration for faster training.
Projects requiring real-time inference capabilities on GPUs.
Developers already using Torch who want to leverage CUDA for performance improvements.
✕ Not a fit for
Projects that do not require or cannot utilize GPU resources.
Teams looking for cloud-based solutions without the need for local setup and maintenance.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Next step
Get Started with cunn
Step-by-step setup guide with code examples and common gotchas.