FAST & PRIVATE ON-DEVICE INFERENCE
Run your models
on user devices
On-device layer for AI model makers and products.
Ship production LLMs to millions of devices
Ship production LLMs to millions of devices
Backed by leading AI investors and builders
Scout
FundScout
FundThomas Wolf
Co-founderLaura Modiano
Startups EMEASiqi Chen
CEOMati Staniszewski
Co-founder, CEOMarcin Zukowski
Co-founder
Backed by leading AI investors and builders
Scout
FundScout
FundThomas Wolf
Co-founderLaura Modiano
Startups EMEASiqi Chen
CEOMati Staniszewski
Co-founder, CEOMarcin Zukowski
Co-founder
Backed by leading AI investors and builders
Scout
FundScout
FundThomas Wolf
Co-founderLaura Modiano
Startups EMEASiqi Chen
CEOMati Staniszewski
Co-founder, CEOMarcin Zukowski
Co-founder
Backed by leading AI investors and builders
Scout
FundScout
FundThomas Wolf
Co-founderLaura Modiano
Startups EMEASiqi Chen
CEOMati Staniszewski
Co-founder, CEOMarcin Zukowski
Co-founder
WHY ON-DEVICE?
On-device inference is the next step for your models.
WHY ON-DEVICE?
On-device inference is the next step for your models.
WHY ON-DEVICE?
On-device inference is the next step for your models.
WHY ON-DEVICE?
On-device inference is the next step for your models.
WHY NOW?
Local models in 2026 can cover most real-world product tasks.
With predictable quality and performance.
Local models cover most practical workloads.
88.7% of AI requests fall into categories that do not require frontier models. Like writing, search, summarization, classification, guidance, extraction.
Consumer hardware can run these models reliably.
WHY NOW?
Local models in 2026 can cover most real-world product tasks.
With predictable quality and performance.
Local models cover most practical workloads.
88.7% of AI requests fall into categories that do not require frontier models. Like writing, search, summarization, classification, guidance, extraction.
Consumer hardware can run these models reliably.
WHY NOW?
Local models in 2026 can cover most real-world product tasks.
With predictable quality and performance.
Local models cover most practical workloads.
88.7% of AI requests fall into categories that do not require frontier models. Like writing, search, summarization, classification, guidance, extraction.
Consumer hardware can run these models reliably.
WHY NOW?
Local models in 2026 can cover most real-world product tasks.
With predictable quality and performance.
Local models cover most practical workloads.
88.7% of AI requests fall into categories that do not require frontier models. Like writing, search, summarization, classification, guidance, extraction.
Consumer hardware can run these models reliably.
WHY US?
Mirai is the fastest execution layer for on-device inference on Apple devices
On-device layer for AI model makers and products.
Connects modern models with consumer hardware, turning local inference into a first-class deployment option.
Peformance