Trusted by more than 7M developers

Local AI native to your Apple device.

Run open models, power AI agents, and connect your favourite developer tools you already use - privately on Apple Silicon.

Partnered by

  • Meta
  • Qwen
  • Liquid AI
  • Baidu
  • Hugging Face
  • Google DeepMind
  • Cohere
  • Z.ai
  • OpenMOSS
  • Gradium
  • IBM
  • OpenBMB
  • Recursive

Docling × MLX-VLM: how IBM Research runs document AI 5.4× faster on a Mac

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End-to-end infrastructure to build and deploy AI that runs locally on user devices.

Everything you need to build local AI, and everything you need to put it in other people's hands.

Build

Design, test, and ship an AI feature entirely on your own hardware. No cloud call, no API key, no bill.

Deploy

Take what you built off your own Mac and put it to work for your team or your users, without it ever touching the cloud.

Running a model locally is only the beginning. Intelligence needs to fit the device, use memory efficiently, respond fast enough, and work reliably inside a real application.

That is what Neywa builds.

You already own a supercomputer

Our engine is built specifically for Apple Silicon

It means it runs locally on your machine, free and offline.

It powers products built by us and other companies.

OUR LIBRARIESPRODUCTS USING THEMmlx-vlmmlx-audioNativ
Nativ

Run AI on your Mac

Run models locally, connect agents, developer tools and apps through standard APIs - no per-token bills, no shipping your data to someone else's cloud.

Trusted by more than 7M developers

Build AI products that run locally

Build applications that can read, see, hear, speak, and create directly on personal hardware.

Our omni engine brings together our work across multimodal model support, inference, quantization, memory optimization, and device-aware performance.

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MLX-VLM and MLX-Audio wrapping Apple Silicon, with Nativ and Nativ Pro built on top

Bring your model on-device.

We work with model labs to turn new architectures into efficient, usable on-device implementations.

Our work can include:

Architecture implementation and model bring-up

Quantization and memory optimization

Vision, audio, video, and multimodal inference

Apple silicon and MLX support

Performance validation across devices

Reference applications and developer examples

Model releases prepared for local adoption

Day-one support for new model launches

The result is more than a successful inference test. It is a model developers can understand, evaluate, and build into products.

Partner with Neywa

Developers from across the world build on us

  • Sinan

    AI video creator

    Istanbul, Turkey

    MLX-VLM

    “Switching to Apple Silicon completely changed how we handle video pipeline at Sinners, and MLX-VLM quickly became core to our stack. It gives us the efficiency to run local video analysis and frame-by-frame scene continuity tracking without relying on cloud APIs. We initially tested it with smaller Qwen models, scaled across different LLMs, and it’s been a permanent part of our workflow ever since.”
  • Daryl

    Entrepreneur

    Honolulu, Hawaiʻi

    MLX-VLM

    “I build offline AI tools for our community in Hawaii, where we experience regular power and internet outages during severe weather. Research into comparing inference engines on Apple Silicon led me to the MLX framework, and I found the speed up from using MLX-VLM to be the most promising development towards building on-device applications to increase the resilience and security of our community.”
  • Jorge de los Reyes

    Founder, En Busca del Fuego

    Madrid, Spain

    Nativ

    “What stood out to me about Nativ was how it makes local AI easier to navigate. Its model library helps you discover the right model for each use case instead of leaving you to figure everything out on your own. For professionals handling privileged, confidential or commercially sensitive information, keeping sensitive workflows local can be extremely valuable. In my view, a hybrid setup (local AI alongside frontier models) offers a strong balance between privacy and capability.”

Bring the datacenter
to your desk

You already have a supercomputer. Run it locally, in the open