Silk AI Enablement: Training Workloads on Azure

Silk handles the vast datasets you need

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Supercharge Your AI Training Workloads on Azure with Silk

Discover how Silk’s high-performance software-defined cloud storage platform revolutionizes AI training workflows in the cloud. This solution brief reveals how Silk eliminates traditional bottlenecks by providing ultra-low latency, massive throughput, and frictionless scalability — all seamlessly integrated with the Azure AI ecosystem. Built for data-intensive workloads like large language models (LLMs), Silk enables faster training cycles, greater reliability, and optimized cloud costs.

  • Get an in-depth look at Silk’s architecture, including data ingestion, compute integration with Azure NDv4-series VMs, and scalable storage solutions designed for high-throughput AI workloads.
  • See how Silk dramatically improves metrics like data access latency and checkpoint save time, with tested results from training a GPT-style model on 100TB of data.
  • Explore how Silk integrates with tools like PyTorch, TensorFlow, Hugging Face, and Azure Machine Learning for seamless orchestration, plus learn how to streamline your AI data pipelines.

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