New Go Deep Learning Framework: “qeep” Launches with GPU Support

Developer Sahand Safizadeh released qeep (pronounced /kēp/), a new deep learning framework written entirely in Go that gives developers low-level control over neural network operations. Released on January 31, 2026, the open-source project combines tensor manipulation with GPU acceleration, offering an alternative approach to building machine learning models in Go’s ecosystem (ℹ️ GitHub).

Background

While Python dominates the deep learning landscape with frameworks like PyTorch and TensorFlow, Go developers have historically faced limited options for building neural networks natively. Most Go-based AI projects rely on bindings to Python libraries or lack comprehensive features like automatic differentiation and GPU support. The launch of qeep addresses this gap by providing a pure Go implementation with production-ready capabilities.

What Happened

The qeep framework introduces several core features that set it apart from existing machine learning tools for Go. The framework can handle multi-dimensional tensors and offers a wide range of linear algebra and statistical functions, automatically calculates gradients with AutoGrad, and uses GPU acceleration through CUDA for faster computations. Using a declarative API, developers can define neural networks while still having fine-grained control at the tensor level.

The framework includes neural network components such as fully connected layers, activation functions (Tanh, Softmax), loss functions (Cross-Entropy), and optimizers (AdamW). Example implementations demonstrate classification tasks like the Iris dataset, showing how developers can build, train, and deploy models entirely in Go.

Why It Matters

This release matters for Go developers who want to build AI applications without leaving their preferred language ecosystem. The combination of AutoGrad, CUDA support, and a declarative API makes qeep competitive with more established frameworks while leveraging Go’s strengths in concurrency and performance.

For teams already invested in Go infrastructure, qeep eliminates the need to maintain separate Python environments for machine learning workloads. The framework’s MIT license and open-source nature also encourage community contributions and customization for specific use cases.

GPU acceleration via CUDA is particularly significant, as it enables developers to scale computations for large datasets and complex neural networks. The framework requires a CUDA-capable GPU, CUDA Toolkit, and CGO configuration, which are standard requirements for production deep learning environments.

What’s Next

The project currently stands at version 0.3.0 with 17 stars on GitHub and includes working examples for classification tasks. The developer has provided documentation for installation, usage, and GPU setup, making it accessible for developers ready to experiment.

Future development will likely focus on expanding the component library, adding support for more neural network architectures, and optimizing performance for production workloads. The open-source community can contribute through GitHub’s standard pull request process.

Developers interested in trying qeep can clone the repository and integrate it into their Go projects using Go modules. The framework provides a declarative stream API that simplifies model definition while allowing low-level tensor operations when needed.

Source: GitHub—Published on January 31, 2026
Original repository: https://github.com/sahandsafizadeh/qeep