AI-Native Edge Intelligence

Frontier reasoning for the places the cloud can't reach.

TinyLLMs distills large-model intelligence into small language models that run on local hardware — offline, in milliseconds, fully under your control.

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Research & engineering from

Stanford UMN Google AWS Azure Nvidia

What we do

We rearchitect intelligence for the edge.

Distill

Compress frontier reasoning from 70B+ teacher models into 1–7B models — without losing the reasoning.

Deploy

Run on local hardware with no cloud dependency, no network, and no data leaving the device.

Operate

Agentic systems for mission-critical, offline environments where latency and reliability are non-negotiable.

TinyLLMs in numbers

<3B

active parameters

12ms

edge inference latency

4GB

minimum hardware footprint

100%

offline & air-gapped

Solutions

Built for environments the cloud was never designed for.

Flagship

Emergency Response & ADAS

On-vehicle agents for traffic preemption, dynamic routing, and dispatcher intent — running locally when responders hit dead zones.

Domain

Field & Industrial Operations

Reasoning copilots for remote sites, factory floors, and contested connectivity — where uptime can't depend on a network.

Domain

Privacy-Critical Inference

On-device intelligence for regulated data that must never leave the hardware it lives on.

Platform

Cloud-scale training. Edge-scale deployment.

A single pipeline takes a frontier model and lands it, intact, on hardware with as little as 4 GB of memory.

01 — Train

Reward modeling & RL

Teacher models trained on H100/A100 clusters with reinforcement learning for spatial and persona reasoning.

02 — Distill

Compress, don't lose

Knowledge distillation, quantization, and pruning shrink the model to edge size while preserving reasoning.

03 — Deploy

Run on the device

Autonomous logic and routing execute locally — zero cloud latency, zero network dependency.

Knowledge Distillation

Transfer behavior from 70B+ teachers into sub-3B students.

Quantization

INT8/INT4 weights — less memory, faster edge compute.

Weight Pruning

Drop non-critical connections to enforce sparsity.

LoRA Finetuning

Freeze the base; train tiny rank-decomposition adapters.

Company

Founders

Two engineers who build across the full stack — from training to edge deployment — and share everything in between.

Prabhjot Singh Rai

Co-Founder

Works across the full stack — RL training, distillation pipelines, and on-device deployment. Research in reward modeling and policy optimization.

Sakthivel Sivaraman

Co-Founder

Works across the full stack — quantization, inference runtime, and training infrastructure. Research in edge computing and NLP.