AI ML workload: Optimising compute for machine learning and AI tasks

Artificial Intelligence has transformed the way enterprises think, operate, and grow. However, these intelligent systems depend on a foundation that can handle massive computational power, low latency, and rapid sAcross industries, Artificial Intelligence and Machine Learning are reshaping how organisations operate, compete, and innovate. From personalising customer experiences to driving research breakthroughs, AI has become a necessity, not a luxury. Yet, as enterprises embrace this transformation, they encounter major challenges around performance, scalability, and cost.

To truly unlock the power of AI, businesses need a secure, scalable, and high-performance compute platform. Tata Communications Vayu AI Cloud provides this foundation, allowing enterprises to develop, train, and deploy AI solutions faster and more efficiently. With cloud-native orchestration, AI development tools, and seamless access to high-performance GPU infrastructure, businesses turn data into decisions with speed, reliability, and confidence.

Understanding AI ML workloads and their enterprise impact

An AI ML workload refers to the complete process of building, training, and deploying an intelligent model. It includes everything from data preparation to real-time model inference. Typically, this process is divided into three main stages.

Develop: This stage involves preparing and cleaning data, selecting algorithms, and transforming datasets to improve accuracy. Teams design and test models, ensuring they can identify meaningful patterns in data.

Train: Once the data is ready, it is fed into the model to teach it how to predict outcomes. This step requires significant computing power to optimise model parameters and improve accuracy using validation datasets.

Deploy: The final stage involves moving the trained model into a production environment, where it can make predictions on new data. Continuous monitoring and retraining help maintain its reliability. Modern AI/ML workloads rely on scalable AI Cloud environments, high-performance BareMetal GPU compute, and elastic GPU Cloud resources to process large datasets and run advanced training pipelines efficiently.

How GPUs accelerate AI ML workloads for high-performance computing

The most powerful AI systems today rely on GPUs, or Graphics Processing Units, to achieve the necessary speed and efficiency. GPUs are designed for high-speed, parallel computing, making them ideal for handling the enormous data and computational demands of AI and ML workload operations.

Tata Communications offers GPU-as-a-Service (GPUaaS) with dedicated BareMetal NVIDIA GPUs, fully integrated with the Vayu AI Cloud platform for development and deployment. This provides dedicated BareMetal GPUs with no resource sharing, ensuring full performance for AI training and inferencing.

At the heart of this platform are NVIDIA GPUs, powered by advanced software such as NIM microservices, Omniverse, and Isaac. Together, they deliver unmatched speed, scalability, and precision for all AI tasks.

Two GPU configurations stand out for enterprises:

AI.H100.IB.8X – Built for extreme speed and scale, this configuration includes eight NVIDIA H100 GPUs in an HGX system, supported by 224 vCPUs and one terabyte of RAM. It features a 3200 Gbps non-blocking Infiniband network that enables lightning-fast GPU-to-GPU communication, essential for training large models like LLMs.

AI.L40S.4X – Designed for versatility, this setup includes four NVIDIA L40S GPUs with 128 vCPUs and 512 GB RAM. It is ideal for image processing, visual analytics, and multi-modal inferencing, combining performance with flexibility.

To keep up with these powerful GPUs, Vayu AI Cloud provides high-speed storage systems, including a Lustre-based parallel file system that supports up to 105 GB per second read speeds and 75 GB per second write speeds. These configurations are delivered through Tata Communications’ GPUaaS, ensuring enterprises get dedicated BareMetal performance for AI training, fine-tuning, and real-time inferencing workloads.

Real-world enterprise applications of AI ML workloads

Enterprises across industries are already transforming operations through optimised AI and ML workloads. Here are a few examples:

• Manufacturing: Predictive maintenance powered by AI helps identify potential equipment failures before they occur, reducing downtime. Computer vision enhances product quality by enabling automated visual inspections during production.
• Retail and consumer goods: AI enables hyper-personalised recommendations and conversational commerce, improving customer engagement. Real-time analytics help manage inventory efficiently, ensuring stock availability across channels.
• Automotive: From autonomous driving to predictive vehicle maintenance, AI models process sensor data in real time to enhance safety and reliability.
• Financial services: AI supports fraud detection, credit scoring, and regulatory compliance. With secure and scalable infrastructure, financial institutions can process data faster and deploy trusted AI solutions.

Beyond these industries, enterprises also benefit from multi-modal AI applications. Using tools like NVIDIA NIMs, the Vayu AI Cloud supports Retrieval-Augmented Generation (RAG), processing text, images, and voice seamlessly. This empowers teams to drive insights and decision-making through faster and more accurate data analysis.

Strategic approaches to scaling AI ML workloads efficiently

Scaling AI workloads efficiently requires more than just powerful hardware. It demands a fully managed platform that simplifies the process while ensuring consistency and reliability. Tata Communications offers this through a unified AI Cloud that covers both cloud and edge environments.

Key features include:

• Cloud-native deployment: On-demand GPUs are available via a CNCF-certified Kubernetes platform. This managed service ensures 99.99 percent uptime and provides effortless scaling for training and inference workloads.
• AI development ecosystem: Developers gain access to tools like AI Workbench for coding, notebooks, and automation, as well as the AI SuperMarket, which offers a library of pre-trained models and APIs for quick deployment.
• MLOps and governance: These tools streamline the entire AI lifecycle, enabling collaboration, version control, and responsible AI governance.
• Serverless AI: This feature eliminates the need to manage infrastructure manually. Models can be trained, fine-tuned, and deployed automatically, with resources scaling on demand.
• Data management: The platform includes tools for organising, versioning, and governing datasets. This ensures consistency and quality across all AI initiatives.

Aligning AI ML workloads with business objectives and ROI

For AI adoption to deliver real business value, it must align with cost, performance, and security goals. Tata Communications helps businesses achieve a predictable low total cost of ownership through flexible pricing and strong governance.

Pricing options include:

On-demand pricing: Offers complete flexibility, allowing organisations to pay only for the resources they use. This model is ideal for short-term projects or workloads that require dynamic scaling without long-term commitments.

Reserved instances: Enable businesses to commit to specific GPU configurations for a defined period to secure significant cost savings. This approach is best suited for predictable, long-term workloads where consistent performance and budget planning are priorities.

Data pipeline efficiency: Vayu AI Cloud optimises data movement through high-speed storage and parallel file systems, enabling fast access to training datasets without manual infrastructure management.

Final thoughts on AI ML workloads

The path from data to insight requires a platform that is unified, efficient, and secure. Tata Communications GPU Cloud Solutions brings all these elements together. It provides the performance of NVIDIA GPUs, the flexibility of cloud-native infrastructure, and the trust of sovereign data security.

By simplifying development, training, and deployment, enterprises can focus on outcomes rather than managing infrastructure. Whether you are scaling AI research, automating operations, or enhancing customer experiences, the Vayu AI Cloud gives your business the agility to innovate faster.

Schedule a conversation to explore how your industry can leverage AI and ML workloads for transformation.