Ludwig vs MLX

Ludwig is an open-source deep learning framework for building, fine-tuning, and deploying models across data types including tabular, text, image, audio, time series, and geospatial data. Users describe preprocessing, model components, training, and hyperparameter optimization in a validated YAML configuration instead of writing training loops. It supports multimodal and multi-task models, plus LLM fine-tuning methods including SFT, DPO, KTO, ORPO, and GRPO, with parameter-efficient options such as LoRA and QLoRA. Training can scale through Ray using DDP, FSDP, or DeepSpeed; Kubernetes and KubeRay are also listed. Hyperparameter optimization integrates Ray Tune and Optuna, with SQLite or PostgreSQL persistence. Models can be served as REST APIs or exported to SafeTensors, ONNX, or torch.export. The framework also offers experiment tracking integrations, generated reports and visualizations, and explainability features. Ludwig is free under the Apache 2.0 License, supports Python, and runs on Linux, macOS, Windows, or self-hosted infrastructure. The FAQ notes that Unsloth may be faster for users focused only on LLM fine-tuning and maximum throughput.

MLX has no write-up on Specifiction yet; its figures are above.