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AI Accelerator

Geniatech AI Accelerator Modules offer a cost-effective, flexible, and ready-to-deploy hardware solution for enhancing edge devices with powerful AI capabilities. Available in M.2 and board-to-board connector formats, these modules seamlessly integrate with existing PCs and edge systems to boost deep learning performance. Designed for scalability, they enable real-time, low-latency AI inferencing across a wide range of industrial and IoT applications. With high performance and low power consumption, Geniatech’s AI accelerator cards provide an efficient, scalable solution tailored to the demands of edge AI deployment.
40 TOPS M.2 AI Accelerator Module

AIM-M-K

40 TOPS M.2 AI Accelerator Module
Edge AI InferenceLow latency
Hailo-8 AI Accelerator Module

AIM-B-H8

Hailo-8 AI Accelerator Module
-40°C to +85°C
Hailo-10 M.2 AI Accelerator Card

AIM-M-H10

Hailo-10 M.2 AI Accelerator Card
40 Tops
MemryX MX3 M.2 AI Accelerator

AIM-M-MX

MemryX MX3 M.2 AI Accelerator
DEEPX M.2 AI Accelerator

AIM-M-DX

DEEPX M.2 AI Accelerator
40 TOPS AI Inference Accelerator with NXP Ara240

AIM-B-K

40 TOPS AI Inference Accelerator with NXP Ara240
Board-to-BoardGenerative AI

What is an AI Accelerator Card/Module?

An AI accelerator module is specialized hardware built to run AI workloads much faster and more efficiently than a standard CPU. Working as a co-processor alongside the main CPU, these modules—typically based on ASICs or dedicated AI chips—handle the heavy lifting of neural network computations, while the CPU manages system tasks. This combination delivers high performance, low latency, and power efficiency, which is especially critical for edge computing applications like industrial automation, robotics, and video analytics.

The Key Advantages of AI Accelerator Modules

  • Exceptional Performance per Watt
    Deliver tens of TOPS at just a few watts. For example, the Hailo-10 M.2 module achieves 40 TOPS under 10W.
  • Rapid Integration and Time-to-Market
    Standard interfaces like PCIe and M.2 allow easy addition to existing x86 or ARM systems, speeding up development and product launch.
  • Deterministic, Low-Latency Performance
    On-chip memory and optimized paths provide predictable, millisecond-scale inference, essential for robotics, automation, and video analytics.
  • Optimized Total Cost of Ownership
    High compute density, low power, and simple integration reduce hardware, cooling, and engineering costs.

Common Form Factors & Interfaces for AI Accelerator Modules

AI accelerator modules are designed to meet diverse deployment needs. The choice of form factor directly impacts integration complexity, performance scalability, and suitability for the target environment.

  • PCIe Cards: Suited for data centers and high-end edge servers, handling heavy AI workloads like large-scale video analytics or LLM inference.
  • M.2 Modules: Compact and plug-and-play, ideal for IoT gateways, industrial PCs, and embedded systems—examples include Hailo-10.
  • Custom AI Modules: Tailored boards for high-volume, cost-sensitive products, used in automotive, robotics, and consumer electronics, such as NVIDIA Orin-based modules.