Deeplearning4j vs MLX
Deeplearning4j
deeplearning4j.konduit.aiMLX
opensource.apple.comSide by side
| Figure | Deeplearning4j | MLX |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| API | Yes | — |
| Self-hosted | Yes | — |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.7 (#4) | 7.6 (#17) |
| License | The Deeplearning4j project is licensed under Apache License 2.0. | The GitHub repository lists an MIT license. |
| Purpose | Eclipse Deeplearning4j is an open-source, distributed deep learning framework for the JVM. | MLX is an array framework designed for efficient and flexible machine learning research on Apple silicon. |
Plans
In their own words
Deeplearning4j
Deeplearning4j is an open-source framework for building, training, and deploying neural networks on the JVM, using Java or Scala. It can compute on CPUs through OpenBLAS and oneDNN, or use CUDA for GPU acceleration. For distributed training, it supports Apache Spark across CPU or GPU clusters. The framework can import models from Keras, TensorFlow, and ONNX, while Python4J provides Python interoperability from Java through CPython embedding. Its wider ecosystem includes ND4J, SameDiff, DataVec, Keras Import, Python4J, OmniHub, and Arbiter. Documented deployment targets include JVM microservices, mobile devices, IoT, and Spark environments. The libraries are licensed under Apache License 2.0 and governed by the Eclipse Foundation. The quickstart recommends Maven for Java projects, with Ivy and Gradle also workable. Documentation covers version 1.0.0-M2.1. The free plan is listed at free. Support options include GitHub issues, community forums, Stack Overflow, and professional support from Konduit.