Delivering AI on Edge
Pioneering running production grade AI on the Edge
Train & Optimize
Model learns from data and establishes its ability to perform a specific task.Optimize it for deployment on edge devices.
Conversion & Interference
After optimization, the model may need to be converted into a format compatible with the target edge device.
Finally, deploy the edge device with the AI model and establish a mechanism for updates and maintenance as needed.
Choose Compute Architecture
- NVIDIA: Known for its high-performance GPUs, NVIDIA has positioned itself as a frontrunner in the AI hardware sector. Products like the NVIDIA A100 and the Jetson series are popular choices for AI development and deployment.
- Intel: As a long-established chip manufacturer, Intel offers a range of CPUs for AI workloads. They have also ventured into the GPU market with products like the Intel Xe, aiming to challenge NVIDIA’s dominance.
- AMD: AMD, renowned for its processors, has introduced GPUs, such as the Radeon Instinct series, to cater to AI and machine learning tasks. Their commitment to providing AI solutions is helping them gain recognition in the AI hardware space.
The Edge Server market has witnessed tremendous growth in recent years and several key players have emerged to meet the growing demand.
We have partnered with the best in the industry to offer you a capable hardware solution.
We offer Dell Edge, Hewlett Packard Enterprise Edgeline services and Lenovo ThinkSystem SE based solutions.
Run Edge AI Models
We have built software capability to run several AI models on edge devices, including but not limited to:
- Convolutional Neural Networks (CNNs): These are commonly used for image and video analysis tasks like object detection, face recognition, and gesture recognition.
- Recurrent Neural Networks (RNNs): These are suitable for sequential data processing, such as speech recognition and natural language processing tasks.
- Lightweight Models: To reduce the computational and memory requirements, there are various lightweight models like MobileNet, SqueezeNet, and Tiny YOLO that can be deployed on edge devices with limited resources.
- Anomaly Detection Models: These models are used for identifying outliers or unusual patterns in data, which can be critical for applications like predictive maintenance in industrial settings.
- Custom Models: You can also develop custom AI models tailored to specific edge computing tasks.
Let’s work together on your
next AI project
The integration of large language models with edge computing represents a fundamental shift in the way we interact with technology. From the seamless voice assistants in our cars to the responsive content on our devices, these applications are transforming user experiences and enhancing operational efficiency across various domains.
This fusion of technologies is not without its challenges, but it’s a testament to the ever-evolving landscape of computing. As hardware improves and software optimization techniques advance, the possibilities for large language models at the edge will continue to expand. This shift will not only redefine how we interact with technology but also how industries operate and innovate.
In essence, it’s a revolution at the edge, and its impact is only beginning to be fully realized. We pioneer in establishing synergy between large language models and edge computing, and how it’s reshaping our digital landscape.