5 min
GPU computing has become one of the most important elements of AI infrastructure today. Owing to inherent architectural advantages, GPUs are well-suited to accelerate Deep Learning tasks. NVIDIA provides a scalable compute infrastructure, ranging from a single GPU in workstations to server-scale end-to-end data centre solutions.
NVIDIA's presence in the market is undisputed. In addition to hardware for parallel procession, NVIDIA provides a cloud based end to end accelerated stack of software, which includes CUDA, CUDA X libraries, drivers, frameworks and much more.
GPU-accelerated computing is helping to solve some of the most complex problems in the world. The AI stack is cutting edge, and it’s evolving all the time, leading to the requirement of a large amount of work to set up and maintain an AI software environment.
NVIDIA GPU Cloud (NGC) provides researchers and data scientists with simple access to a comprehensive catalog of GPU-optimized software tools for deep learning, machine learning and high-performance computing. NGC provides a range of options that meet the needs of data scientists, developers, and researchers with various levels of AI expertise.
To apply for the workshop, click here.
The platform allows researchers to deploy a comprehensive deep learning infrastructure quickly and effortlessly, in easy-to-use, dockerised containers. Advancements such as the creation of tensor cores and the easily-programmable CUDA interface have only served to increase the effectiveness of NVIDIA GPUs for general purpose computing.
Analytics India Magazine has teamed up with NVIDIA to bring to data scientists a workshop focused on tools they need for GPU acceleration. Attendees will be able to learn how to implement GPU acceleration in everyday ML and DL tasks.
To apply for the workshop, click here.
Titled 'NVIDIA GPU For End-To-End Machine Learning Acceleration Workshop', the workshop will offer industry insights on how to make your everyday deep learning tasks more efficient. The workshop will include a keynote conducted by Sundara Ramalingam Nagalingam, Head, Deep Learning Practice, NVIDIA Graphics Pvt Ltd. The workshop will take the participants through the following topics:
- Accelerated Data Analytics for Better Insight & Use Cases
- RAPIDS Deep Dive
- Accelerating Data Science End-to-End with GPU & Getting Started with NVIDIA GPU Cloud
- Data ETL Pipelining Hands-on with cuDF
- XGBoost on MultiGPU Demo and Discussion
- Running other Algorithms on GPU Hands-on
- Q&A Session
- Data Engineers & Data Scientists looking to supercharge their training and inference workflows
- Data Science managers looking for an upgrade to existing infrastructure
- AI/ML enthusiasts with experience in basic concepts of ML, data science, workflows and have worked with Python, Scikit-learn, or Pandas
- From Humanoid Robots to Telecom Trials, 12 Major Announcements at NVIDIA GTC Berlin 2026
- Anyone Can Make a Robot Move, Few Can Make It Stop
- After ISRO’s PSLV Failure, TakeMe2Space Looks to SpaceX for Reliable Launch
- Why AI Agents are the Next Frontier in Quantum Security
- Physical AI Squeezes Labour Market, And Low-Skilled Immigrant Jobs May Be First to Fall
- Rocket Math Isn’t Mathing: Why India’s Space Launches are Expensive

