The fastest inference runtime for iPhone, iPad and Mac.
Optimize and run your model on every Apple device. Up to 38% faster prompt processing vs MLX.

Run your model on 2 billion
Apple devices.
Perfect for:
Model companies.
You train and ship models. Mirai optimizes them for Apple Silicon, benchmarks on real hardware, and distributes.
AI researchers & labs.
Mirai converts your model and puts it in front of real users on Apple devices, not just leaderboards.
Independent makers.
You're fine-tuning or training from scratch. Mirai gives your model the same device reach as OpenAI and DeepSeek.
What Apple Silicon delivers today with Mirai.
Convert. Integrate. Run.

One inference engine. Integrate from any language.
cargo add uzu --git https://github.com/trymirai/uzuhttps://github.com/trymirai/uzu.gitpnpm add @trymirai/uzuuv add uzu- Same high-level API across all languages.
- Full performance of the Rust core from every language.
- Convert once, integrate anywhere.
Built-in features every model gets automatically:

Supported models:
Common questions:
Our stack consists of two main products:
It takes original model checkpoints and converts them into a format supported by the inference engine. This format is an intermediate representation composed of unified blocks, each backed by a corresponding encoded sequence in the inference engine. If your model is built using already supported blocks, nothing special is required, you can simply run the conversion command yourself. If your model introduces custom layers, we can easily add support for them (and you’re welcome to implement it yourself as well).
Each unique block from the intermediate representation has corresponding backend-specific kernels, which are executed on the target compute device during inference. Similar to the conversion toolkit, if your model introduces a new type of layer, the corresponding kernels will need to be implemented.
We currently support a wide range of architectures. You can find the full list of supported models here:
Full list of local modelsAs shown in our benchmarks, Mirai significantly outperforms other inference engines on edge devices. The performance boost varies depending on the specific model-device pairs. We also provide out-of-the-box speculative decoding. In this setup, proposals are generated using a very small model (<50MB), allowing it to run even on iOS devices.
This fully depends on device capabilities. On macOS, very large models such as GPT-OSS can run depending on available memory. On iOS, we recommend staying under 3GB of RAM usage. Quantized 4B models are typically a great fit.
Our inference engine includes a built-in CLI tool with a dedicated benchmark command. You can find more details here:
Link to the CLI toolFeel free to reach out via email.
contact@trymirai.comYou can also join our Discord:
Mirai DiscordOr book a call with our engineers. We’d be happy to discuss your specific requirements:
Book a call