Use cases
1
Distributed deep learning workloads
Enable large-scale distributed training by providing shared dataset access across multiple GPU nodes using frameworks such as PyTorch and TensorFlow.
2
Largescale AI/ML model training and finetuning
Train and fine-tune large language, vision, and multimodal models with concurrent dataset access across dozens or hundreds of GPUs.
3
High performance data pipelines
Enable preprocessing, feature engineering, and checkpointing workflows without slowing down GPU execution or interrupting compute-intensive tasks.
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Brief
Tata Communications Vayu AI Cloud - Unified, effortless, trusted
AI is now a necessity, driven by data growth and Gen-AI adoption. Tata Communications Vayu AI Cloud accelerates innovation with scalable, secure platform solution, bringing AI solutions to life.
Datasheet
Tata Communications Vayu AI Cloud- GPU as a service datasheet
GPU-as-a-Service delivers scalable, high-performance GPUs for AI/ML workloads. Optimise training, inference, and deployment with on-demand access—no upfront infrastructure costs.
