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MLD-R1 AI USB

Plug-in on-device USB-type AI accelerator

MLD-R1 AI USB

Plug-in on-device USB-type AI accelerator

MLD-R1 AI USB

Plug-in on-device USB-type AI accelerator

Keep your existing pipeline

Keep your existing pipeline

Keep your existing pipeline

Whether you're deploying a PyTorch model on a Windows laptop or running TensorFlow

inference on an ARM-based industrial PC, MLD-R1 connects and runs.

Whether you’re deploying a PyTorch model on a Windows laptop or running TensorFlow

inference on an ARM-based industrial PC, MLD-R1 connects and runs.

Whether you’re deploying a PyTorch model on a Windows laptop or running TensorFlow

inference on an ARM-based industrial PC, MLD-R1 connects and runs.

AI Frameworks (via SDK qb)

AI Frameworks (via SDK qb)

Keras

Keras

TensorFlow / TensorFlowLite

TensorFlow / TensorFlowLite

PyTorch

PyTorch

ONNX

ONNX

Operating Systems*

Operating Systems*

Windows 11

Windows 11

Linux 22.04.5

Linux 22.04.5

*To be updated

*To be updated

Host Architectures

Host Architectures

x86

x86

ARM

ARM

Powered by the ultra-low-power REGULUS AI SoC

Powered by the ultra-low-power REGULUS AI SoC

~4 W power draw, engineered for 24/7 operation

~4 W power draw, engineered for 24/7 operation

Powered by the ultra-low-power REGULUS AI SoC

~4 W power draw, engineered for 24/7 operation

Turn your device into an
AI inference machine

Turn your device into an
AI inference machine

Turn your device into an
AI inference machine

One stick delivers 10 TOPS (dense, INT8) of on-device AI inference

over a USB port. No chassis swap required.

One stick delivers 10 TOPS (dense, INT8) of on-device AI inference

over a USB port. No chassis swap required.

Accelerate vision to generative AI workload at the edge

Accelerate vision to generative AI workload at the edge

Accelerate vision to generative AI workload at the edge

Mobilint’s NPU architecture has been validated with over

490 open- and closed-sourced models.

Mobilint’s NPU architecture has been validated with over

490 open- and closed-sourced models.

Mobilint’s NPU architecture has been validated with over

490 open- and closed-sourced models.

Specifications

Specifications

Specifications

MLD-R1 AI USB being slipped into a shirt pocket

AI performance

10 TOPS (Dense, INT8)

CPU

Quad-core ARM Cortex-A53, up to 1.2 GHz

Power Consumption

~4 W

Memory

4 GB / 8 GB LPDDR4X at 4,266 MT/s

Supported OS

Windows, Linux

Supported Frameworks

Keras, TensorFlow, TensorFlow Lite, PyTorch, ONNX

LED Indicators

1 (Power LED)

Host Architectures

x86, ARM

Ingress Protection

IP65 (TBD)

Data Interface

USB 3.1 Gen1

Dimensions (mm)

90 ✕ 40 ✕ 16

Weight (g)

88

MLD-R1 goes where MLD-R1 fits

MLD-R1 goes where MLD-R1 fits

MLD-R1 goes where MLD-R1 fits

Forklift Safety

Forklift Safety

Forklift Safety

Real-time pedestrian detection, hard-hat recognition, collision-prevention alerts.

Real-time pedestrian detection, hard-hat recognition, collision-prevention alerts.

AGVs and AMRs

AGVs and AMRs

AGVs and AMRs

Onboard vision and generative AI inference for autonomous mobile robots. Sub-10 W draw compatible with battery-powered operation.

Onboard vision and generative AI inference for autonomous mobile robots. Sub-10 W draw compatible with battery-powered operation.

Construction Machinery

Construction Machinery

Construction Machinery

Restricted-zone intrusion detection and hazard monitoring on moving equipment at construction sites.

Restricted-zone intrusion detection and hazard monitoring on moving equipment at construction sites.

Industrial Safety Monitoring

Industrial Safety Monitoring

Industrial Safety Monitoring

Worker safety monitoring across plants and logistics centers.

Worker safety monitoring across plants and logistics centers.

Speed up model validation and
edge prototyping on your
existing machine, wherever
you are

Speed up model validation and
edge prototyping on your
existing machine, wherever
you are

Speed up model validation and
edge prototyping on your
existing machine, wherever
you are

Run models on portable diagnostic devices without network

connectivity.

Run models on portable diagnostic devices without network

connectivity.

Run models on portable diagnostic devices without network

connectivity.