mlx
A framework for machine learning on Apple silicon.
Decision gist · record as of 2026-08-14
Yes, if you develop on Apple silicon and want a research-grade ML framework with low data-transfer overhead and NumPy-familiar APIs. The active maintenance, permissive license, and lack of known vulnerabilities support adoption. Medium install friction and Apple-silicon focus limit appeal to Linux/Windows-only teams, but the framework is stable and well-supported for its target platform.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and Apple silicon (or Linux/Windows with appropriate backend); mlx-metal runtime dependency must be available.
- Medium install friction due to platform-specific wheels and a compiled runtime dependency (mlx-metal).
- Actively maintained with recent releases; last commit 2026-08-14 and 27951 repository stars indicate ongoing development.
License · maintenance · safety
MIT (permissive) — MIT license is permissive, allowing commercial and private use with minimal restrictions—typical for research-backed frameworks.
last release 2026-07-07 (38 days) · last repo commit 2026-08-14 · 27,951 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,327,045 downloads/mo, #4,055 on PyPI
Alternatives
Verify before relying
pip install mlx
import mlx.core as mx
arr = mx.array([1, 2, 3])
result = mx.sum(arr)- Whether the framework's performance on Apple silicon compares favorably to PyTorch or JAX for typical workloads.
- Maturity and stability of the C++, C, and Swift APIs relative to the Python API.
- Community size and availability of third-party extensions or pre-trained models.
What it is and what it does
MLX is a machine learning framework designed by Apple for efficient computation on Apple silicon. It provides a Python API closely mirroring NumPy, with higher-level modules like mlx.nn and mlx.optimizers that follow PyTorch conventions. The framework supports composable function transformations for automatic differentiation and vectorization, lazy evaluation of computations, and dynamic graph construction that avoids slow recompilation when argument shapes change.
MLX's key architectural distinction is its unified memory model: arrays live in shared memory and operations can run on CPU or GPU without explicit data transfers. The framework is intended for researchers who want both user-friendly APIs and efficient training and deployment on Apple hardware. It has C++, C, and Swift APIs alongside Python, and supports multi-device operations across supported hardware.
Use it for
- Training transformer language models or fine-tuning large language models like LLaMA on Apple silicon without data transfer overhead.
- Prototyping machine learning research with dynamic computation graphs that adapt to changing input shapes without recompilation.
- Running inference for generative tasks (text, image, speech) on macOS with GPU acceleration via unified memory.
- Building cross-platform ML applications using MLX's C++, C, or Swift APIs alongside Python.
- Developing ML workflows that leverage automatic differentiation and vectorization for complex model architectures.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you develop on Apple silicon and want a research-grade ML framework with low data-transfer overhead and NumPy-familiar APIs.
The active maintenance, permissive license, and lack of known vulnerabilities support adoption. Medium install friction and Apple-silicon focus limit appeal to Linux/Windows-only teams, but the framework is stable and well-supported for its target platform.
Install
mlx on PyPI
Before you install
Medium install friction due to platform-specific wheels and a compiled runtime dependency (mlx-metal). Actively maintained with recent releases; last commit 2026-08-14 and 27951 repository stars indicate ongoing development.
Requires Python >=3.10 and Apple silicon (or Linux/Windows with appropriate backend); mlx-metal runtime dependency must be available.
License in practice
MIT license is permissive, allowing commercial and private use with minimal restrictions—typical for research-backed frameworks.
Quickstart
pip install mlx
import mlx.core as mx
arr = mx.array([1, 2, 3])
result = mx.sum(arr)Verify before relying
- Whether the framework's performance on Apple silicon compares favorably to PyTorch or JAX for typical workloads.
- Maturity and stability of the C++, C, and Swift APIs relative to the Python API.
- Community size and availability of third-party extensions or pre-trained models.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagemlx-metal |
| Maintenance | Actively maintained 38 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,327,045 / month, #4,055 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: mlx-0.32.0-cp310-cp310-macosx_14_0_arm64.whl; mlx-0.32.0-cp310-cp310-macosx_15_0_arm64.whl; mlx-0.32.0-cp310-cp310-macosx_26_0_arm64.whl; mlx-0.32.0-cp310-cp310-manylinux_2_35_aarch64.whl; mlx-0.32.0-cp310-cp310-manylinux_2_35_x86_64.whl; mlx-0.32.0-cp310-cp310-win_amd64.whl; mlx-0.32.0-cp311-cp311-macosx_14_0_arm64.whl; mlx-0.32.0-cp311-cp311-macosx_15_0_arm64.whl; mlx-0.32.0-cp311-cp311-macosx_26_0_arm64.whl; mlx-0.32.0-cp311-cp311-manylinux_2_35_aarch64.whl; mlx-0.32.0-cp311-cp311-manylinux_2_35_x86_64.whl; mlx-0.32.0-cp311-cp311-win_amd64.whl; mlx-0.32.0-cp311-cp311-win_arm64.whl; mlx-0.32.0-cp312-cp312-macosx_14_0_arm64.whl; mlx-0.32.0-cp312-cp312-macosx_15_0_arm64.whl; mlx-0.32.0-cp312-cp312-macosx_26_0_arm64.whl; mlx-0.32.0-cp312-cp312-manylinux_2_35_aarch64.whl; mlx-0.32.0-cp312-cp312-manylinux_2_35_x86_64.whl; mlx-0.32.0-cp312-cp312-win_amd64.whl; mlx-0.32.0-cp312-cp312-win_arm64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “machine learning on Apple silicon”
- mlxMLX is an array framework for machine learning on Apple silicon that…
- mlx-whisperRuns OpenAI's Whisper speech recognition models on Apple silicon…
- pyobjc-framework-CoreMLProvides Python bindings to Apple's CoreML framework on macOS,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also mlx-lm · mlx-whisper · autoray · torch · mlx-audio · mlx-vlm · jax · fal-client · cupy-cuda12x · coremltools