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AI Starts Here.

Mobilint designs the silicon and systems that bring real-time AI inference to the edge — no cloud, no GPU, no server required.

80 TOPS3W TDP400+ ModelsCES Award Winner

What Is a Neural Processing Unit?

A chip built from the ground up to run AI models on-device. Unlike GPUs repurposed for AI, an NPU is architected for neural network workloads — delivering dramatically better performance per watt at the edge.

On-Device Inference

No cloud round-trip. Millisecond response times.

Data Privacy

Visual data never leaves the device.

Low Power

Full AI inference in a 3–25W envelope.

No Network Needed

Works in air-gapped and bandwidth-limited environments.

Your Models Already Work

If you've trained models on GPUs using standard frameworks, they run on Mobilint hardware with no retraining. Export your model, point SDK qb at it, and the compiler handles the rest — optimizing and quantizing for the NPU automatically.

Train on GPU

Use your existing workflow

Export Model

PyTorch, TF, ONNX, TFLite, Keras

Compile with SDK qb

Auto-quantize to INT8 at 99% accuracy

Deploy to NPU

C++ or Python runtime

No retraining

Your GPU-trained model compiles directly. No architecture changes, no new training runs.

400+ validated architectures

ResNet, YOLO, MobileNet, EfficientNet, transformers, and more tested and verified on Mobilint silicon.

Run multiple models simultaneously

ARIES supports up to 32 concurrent models on a single chip — run detection, classification, and tracking in parallel.

TOPS per chip

Lowest TDP

CES Innovation Awards

ARIES NPU Inference Performance

MobileNetV211,551 FPS

ResNet-503,082 FPS

YOLO-11s784 FPS

YOLO-11l259 FPS

★

CES 2025 Innovation Award

REGULUS AI SoC

★

CES 2026 Innovation Award

MLX-A1 Edge AI Box

Where Mobilint Deploys

Robotics & Autonomous Systems

Real-time object detection, path planning, and sensor fusion running directly on the robot. Mobilint NPUs deliver deterministic inference at the edge, eliminating the latency and connectivity dependencies that make cloud-based AI impractical for autonomous navigation.

MLA100 MXMMLX-A1REGULUS

Start Evaluating Today

No custom hardware needed. The MLA100 is a standard low-profile PCIe card — plug it into any server or workstation and start running your models immediately. No proprietary chassis, no special cooling, no infrastructure changes.

Fits any standard PCIe x8 slot

Single slot, low profile — no external power

Ships with SDK qb and sample models

Ubuntu and Windows supported

Request an Evaluation Unit →

Product Catalog

Explore the full range of Mobilint hardware and software.

7 products

AI Accelerator Chip

ARIES NPU

The core silicon. 8 NPU cores, RISC-V CPU, LPDDR4X memory controller — 80 TOPS at 25W.

PCIe Accelerator Card

MLA100 PCIe

Single-slot, low-profile AI accelerator card. 80 TOPS at 25W — bus-powered, no external connector.

Embedded AI Module

MLA100 MXM

82×70mm embedded AI module in MXM Type A form factor. 80 TOPS, -35°C to 75°C operating range.

Edge AI Box

MLX-A1

Turnkey edge AI mini PC — Intel i5 + ARIES NPU (80 TOPS), 32GB DDR5, 3× 2.5GbE. CES 2026 Award.

PCIe Accelerator Card

MLA400

Quad-ARIES PCIe card — 320 TOPS combined AI performance, 128 concurrent models. Coming soon.

AI System-on-Chip

REGULUS

Complete AI SoC in 17×17mm — quad ARM Cortex-A53, 10 TOPS NPU, 4K codec, 8MP ISP at just 3W.

Software Development Kit

SDK qb

Full-stack compiler & runtime for Mobilint NPUs. Compile from PyTorch, TensorFlow, ONNX. MLPerf verified.

Ready to evaluate Mobilint hardware?

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