Imagine your AI projects stalling because the processor cannot keep up. The Forlinx FAI-ARA240-M, M.2 40 eTOPS 16GB LPDDR4 AI Acceleration Card gives you a fast, dedicated engine that sits in a standard M.2 slot. It is built for developers who need extra compute without redesigning their whole board. If you have hit the limits of your i.MX8M Plus or i.MX95 host, this card removes the bottleneck.
Forlinx FAI-ARA240-M, M.2 40 eTOPS 16GB LPDDR4 AI Acceleration Card
The card solves the problem of insufficient AI throughput on edge devices. When a model runs slowly, the user experience suffers and power consumption rises. This matters because real-time inference is often required for vision, speech, or control tasks. By adding 40 eTOPS of compute and 16 GB of fast LPDDR4 memory, the card delivers the performance needed while keeping the system compact.
Key Features
- NXP Ara240 processor delivering 40 eTOPS of AI performance.
- 16 GB LPDDR4 RAM for high-capacity model storage.
- M.2 M-Key interface for easy installation.
- Dimensions 22 mm 80 mm (M.2 2280) for compact designs.
- Supports i.MX8M Plus and i.MX95 host SoCs.
- Runs on Linux operating system.
- Compatible with TensorFlow, PyTorch, and ONNX frameworks.
- Handles CNNs, Transformers, LLMs, MMLMs, VLMs, and VLAs.
- Provides real-time inference for vision and language tasks.
- Designed for low power consumption in edge environments.
Specifications
| Hardware Parameters of Ara240 AI Acceleration Card |
| Processor |
NXP Ara240 |
| RAM |
16GB LPDDR4
|
| Interface |
M.2 M-Key |
| Dimensions |
M.2 2280 (22mm x 80mm) |
| Supported Host SoC |
|
| AI Performance |
40 eTOPS (equivalent TOPS) |
| Operating System |
Linux |
| AI-Model Frameworks Supported |
- TensorFlow
- PyTorch
- ONNX (Open Neural Network Exchange)
|
| AI Model Architectures Supported |
- Convolutional Neural Networks (CNNs)
- Transformer Models
- Large Language Models (LLMs)
- Multimodal Language Models (MMLMs)
- Vision Language Models (VLMs)
- Vision Language Actions (VLAs)
|
