WHO IT'S FOR?
Built for companies who ship models.
Model makers (LLMs, audio, multimodal).
Infra and systems engineers.
Teams pushing inference out of the cloud.
WHAT WE DO?
Extend your existing cloud pipeline to devices.
Models stay the same.
Execution moves local.
WHY?
Predictable performance.
Lower time-to-first-token
Stable latency
Reduced memory usage
No network round trips.
No cloud dependency at runtime.
WHY NOW?
Inference is becoming infrastructure.
Modern Apple devices can run real workloads locally.
Inference is no longer just deployment, it’s a system layer.
What Mirai does
Mirai extends your existing pipeline to devices.
Model makers (LLMs, audio, multimodal).
Infra and systems engineers.
Teams pushing inference out of the cloud.
Real-time audio
Speech-to-text and text-to-speech without round trips to the cloud
Modern devices can now execute meaningful AI workloads locally
Inference is becoming the execution layer of AI software
Mirai outperforms:
Apple MLX
llama.cpp
Benchmarks
MLX
Token generation speed
Measured in tokens per second (Higher tokens/sec means faster responses, smoother UX, and fewer dropped devices)
Token generation speed
Prefill speed
Time to first token
Memory usage
Model: llamba-1B
Device: Apple M1 Max
32gb
Route each request to the right place, device or cloud, using the same API. Fast, private local inference. Scalable cloud compute when needed.
We’ve partnered with Baseten to give full control over where inference runs. Without changing your code.
Drop-in SDK for local + cloud inference.
Model conversion + quantization handled.
Local-first workflows for text, audio, vision.
One developer can get it all running in minutes.
Deploy and run models of any architecture directly on user devices
Choose which developers can access models.