Industry veteran with over 10 years of experience architecting and managing GPU-powered cloud solutions.
Specializes in enabling scalable AI/ML and HPC workloads for enterprise and research applications.
Former lead solutions architect for top-tier cloud providers and startups in the AI infrastructure space.
Using GPU servers for climate modeling embedded with vast geological, geographical and anthropomorphic information, and interspersed with millions of variables, is right in the ballpark of Graphical Processing Units (GPUs). Why GPUs though? Let’s find out. What is Climate Modelling? Climate models are computer programs coded to run quantitative cause-and-effect simulations in response to any […]
Even if you’ve been an ostrich when it comes to global happenings and tech news, you must have certainly heard about blockchain technology? Maybe, you even considered it a tech fad meant for cryptocurrency aficionados only? Well, this article will not only help dispense your scepticism but also will bring to the forefront the power […]
The necessity of using Graphics Processing Units (GPUs), especially Cloud GPUs, for handling compute-intensive predictive analytics workloads and associated ML algorithm development.
GPU (Graphic Processing Unit), as the name suggests, is a processor – a specialized processor! It was built to support graphics rendering workloads. In today’s world of burgeoning graphical applications requiring ultra-high resolution, 3D simulation and image recognition, the GPU is revolutionizing the landscape.
Insurance companies must reduce their dependence on manual techniques for identifying valid claims. Many insurance companies have already begun leveraging emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) to identify fraudulent claims.
Artificial Intelligence (AI) is now intersecting human lives in myriads of previously unimaginable ways. Tremendous advancements are being made every day in the fields of Deep Learning (DL), Generative Artificial Intelligence (GAI) and Convolutional Neural Networks (CNNs).
Route optimization has a substantial role in logistics efficiency and ensuring that deliveries will arrive on time. This also eventually ties into minimizing transportation, fuel and manpower costs.
Healthcare firms and gene research institutes are inclining towards GPU computing to enhance their analytics performance. GPU acceleration simultaneously addresses the challenge of high-density data processing while also facilitating scalability and real-time availability.
Graphics Processing Units (GPUs) have become indispensable for enterprise-grade systems handling heavy computational workloads. However, enterprises often end up oversubscribing to GPU hardware, leading to wasted processing power. This is especially true when running applications that do not fully utilize the available memory or compute capability of the GPU cluster.
AI/ ML based banking software must necessarily be trained on colossal amounts of customer information and market data, and then further refined in real-time by observing ongoing banking operations. Such substantial ML training naturally necessitates the utilization of GPUs to leverage their multi-core parallel computing for expediting data processing and enhancing throughput.