MLX

ml-explore.github.io · ML Frameworks

Open source

#16 in ML Frameworks

Apple's array framework for machine learning on Apple silicon, with unified memory and a NumPy style API.

#framework#apple-silicon#arrays#on-device

MLX is one of 20 ML framework tools on Axiomi. See all ML framework tools or how we check facts.

// guides

Guides that cover MLX

Ollama vs vLLM: Which Should You Run?
Ollama vs vLLM compared: setup, model formats, speed for one user or many, hardware, Mac support and the signs it is time to move from Ollama to vLLM.
How to Run ComfyUI on Mac (Apple Silicon)
How to run ComfyUI on Mac: install Comfy Desktop or set it up by hand with PyTorch and Metal, where model files go, what fits in memory and how to fix errors.
Tinygrad vs MLX: Which Should You Use?
Tinygrad vs MLX compared for Apple silicon and beyond: hardware support, lazy graphs, JIT and compile, LLM tooling, install and which framework fits your project.
Best ML Frameworks for Python in 2026
The best ML frameworks for Python in 2026, ranked by job: PyTorch, scikit-learn, JAX, Keras 3, XGBoost, LightGBM, TensorFlow, MLX and more, with versions.
How to Run Muse Spark Open Locally (816B MoE)
Can you run Muse Spark Open locally? Real memory needs by quant (GGUF, MLX, NVFP4), llama.cpp and MLX commands, Mac Studio setups, licence terms and limits.
Tinygrad vs PyTorch Performance
Tinygrad vs PyTorch performance: when TinyJit and BEAM can outrun eager PyTorch, when torch.compile and the ecosystem win, and how to compare them fairly.

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