Physical AI · Data Center · Cloud

We make the chips
that run AI everywhere

One chip family, from a 3-watt sensor to a full server rack — on a fraction of a GPU's power.

2025 Edge AI & Vision Alliance Product of the Year award 2025 Edge AI & Vision Alliance Product of the Year Award
MemryX MX3 AI accelerator chip, close up
Scroll

Built for inference at every scale. One dataflow architecture runs from a 3 W chip at the sensor to a full rack in the data center — no rewrites, no compromises.

One toolchain from edge to cloud. Our compiler maps any model onto the fabric automatically, delivering deterministic latency and order-of-magnitude power efficiency on every request.

Industry solutions

Where MemryX goes to work

The same architecture, deployed across every environment that needs inference at the edge.

Explore solutions →

Industrial & robotics

Manufacturing, automation and edge computing on the factory floor.

Robotic arms on an automated manufacturing line

Video management

AI-powered surveillance and smart vision at scale.

Security camera monitoring an urban scene

Transportation

Automotive and mobility applications, from cabin to roadside.

Vehicles moving through a city intersection

Smart devices

On-device intelligence for connected edge products.

Connected smart devices in a home environment
Why MemryX wins

Order-of-magnitude efficiency

A dataflow, at-memory architecture streams data between processing elements — cutting the power-hungry memory access that limits GPUs and CPUs.

Efficiency
>100×
Performance per watt
Measured against comparable GPU inference accelerators.
Economics
>100×
Inferences per dollar
No external DRAM, no thermal overhead, no idle silicon.
Throughput
20×+
Performance vs CPU / GPU
Deterministic latency at a fraction of the power budget.

Inferences / second per watt — MX3 vs GPUs

NVIDIA AGX Orin
3.9
NVIDIA RTX A2000
3.9
NVIDIA RTX 4060
5.2
MemryX MX3
77.6
MemryX-published figures, YOLOv8 workload. Values are illustrative here until confirmed for the live site.
Products

Three ways to get MX3 into your product

Start with the bare chip, drop in a ready-made module, or deploy a finished box — same architecture and the same toolchain, whichever you pick.

MemryX MX3 AI accelerator chip MemryX MX3 chip, isolated straight-on view MemryX MX3 chip, close up

Chips

The MX3 AI accelerator — 6 TFLOPS at roughly 3 W. The dataflow core everything is built on.

Explore →
MemryX Cascade M.2 accelerator module MemryX Cascade 100R Raspberry Pi 5 HAT+

Modules & form factors

The Cascade 100 family — Raspberry Pi HAT+, USB-C, M.2 and PCIe. From 12 to 100 TFLOPS.

Explore →
MemryX ready-to-deploy edge inference system

Systems

Ready-to-deploy edge and server systems for production inference at scale.

Explore →
How it fits

Acceleration that fits your workflow.

Keep what works. Accelerate what matters.

The usual route

Rebuild around the hardware

Retrain

Re-quantize

Port frameworks

Rewrite the app

Re-validate

Rebuild

With MemryX

Compile. Accelerate. Run.

Your stack stays as it is.

Your models

PyTorchTensorFlowONNX
Yours

Compile

MemryX

Run on MX3

MemryX

Your application

Yours

↑Higher performance

↑Greater efficiency

→Ready to scale

More AI capability. Less disruption.

Proof

Independently reviewed

Not our claims — theirs. Five independent labs, reviewers and publications put the MX3 through its paces.

“… the 1st AI accelerator we’ve encountered for which both the hardware and the software just works … exceptionally easy to use while providing good performance and consuming little power.”

Independent benchmarking evaluation

“It’s a low-power, high-efficiency workhorse … it integrates seamlessly into existing systems — bringing massive AI capability without the usual hardware overhaul.”

Hardware review

“When it comes to edge AI, the MX3 M.2 AI Accelerator Module punches way above its weight — literally.”

Hands-on module test

“I tested a lot of NPU accelerators. And in my opinion this one [MemryX] is the most convenient one.”

Anton Maltsev, Cherry Labs

“When compared to other AI flows, MemryX finished the crossing line in first place …”

Community build review
Technology roadmap

A new generation every two years

The evolution of the core technology — scaling AI compute from millions to billions of parameters.

MemryX MX3 accelerator chip
MX3
2025
In production
Production-grade physical AI at scale, 12 nm.
MX3+
Next generation
More parameters, higher performance per watt.
MX4
Longer term
Generative AI — LLM, VLM and multimodal, on edge and in the data center.
◄ Physical AI — millions of parameters Generative AI — billions of parameters ►
Leadership

Proven semiconductor leadership

Founded 2019 in Ann Arbor, Michigan. Built by people from Nvidia, Onsemi, IBM and the University of Michigan.

Ross Jatou
Ross Jatou
President & CEO
Former VP of Engineering at Nvidia; SVP/GM at Onsemi.
Dr. Wei Lu
Dr. Wei Lu
Co-founder & CTO
Memory and neuromorphic pioneer, University of Michigan; IEEE Fellow.
Dr. Mohammed Zidan
Dr. Mohammed Zidan
VP, Architecture
Led MemryX NPU HW/SW co-design; 17 granted US patents.
Joe Faris
Joe Faris
VP, Sales & Marketing
20+ years in semiconductor and vision-systems leadership.
Ecosystem

A broad partner network

Hardware partners
Software partners
Distributors — where to buy

Ready to build?

Talk to our team, or start today in the Developer Hub — the full toolchain is a free, ungated download.