NVIDIA CUDA® Cores support general-purpose computing with GPU acceleration. Partnering with Tensor Cores to speed up AI inference and training, alongside RT Cores, which employ real-time ray tracing to boost cinematic image rendering performance and speed, ADLINK’s embedded GPU solutions drive better, faster results.
A mere one-fifth the size of PCIe graphics cards, ADLINK’s embedded MXM GPU modules minimize the footprint of the host system. The modules are designed to withstand the harsh physical conditions of an industrial environment, including vibrations, shocks, and extreme temperatures. ADLINK’s embedded MXM GPU modules are also easy on energy, with power consumption starting from just 20 watts.
There is a growing need for GPUs at the edge. Compared with traditional graphics cards, ADLINK embedded MXM graphics cards offer similar performance, but is more power-efficient and only one-fifth the size. Also, they can survive severe temperature extremes, shock and vibration. ADLINK embedded MXM graphics cards can deliver AI inference and compute acceleration to size, weight and power constrained edge applications.
The need for computing is growing exponentially at the edge. The market size is expected to reach USD 155.9 billion by 2030, expanding at a CAGR of 38.9%*. Leveraging the power of GPUs enables industries to better position for edge computing. Download the infographic to learn seven ways to facilitate the edge transition with embedded MXM GPU modules.
Bringing AI to the edge provides many benefits, including faster response time, enhanced security, improved mobility, and lower communication costs. As combinations of neural networks and frameworks, running on specialized computing cores, are ideal for specific tasks, heterogeneous computing is the best strategy for deploying AI. Download the solution brief to learn how to optimize an AI platform with CPU, GPU, FPGA, and ASIC.