Strategic Solutions for AI Workload Acceleration and Cost Management

Recent strides made in artificial intelligence (AI) have opened innumerable opportunities for innovation and growth. Owing to advancements in technologies and methodologies, AI models are tackling increasingly greater generative and operational challenges.

However, this surge in AI’s capabilities has drastically increased the demand for compute, storage, and networking resources, undermining gains made in efficiency. The estimated costs for compute involved in the final training run of large-scale ML systems was over $1 million in 2023, compared to approximately $100,000 in 2018*. Such ballooning costs have created a pressing need for solutions that align costs to business value.

In this white paper, you will learn about:

• The latest trends in the AI and ML market
• Issues with on-prem and cloud-based model training and inference
• Best practices for reducing AI costs without hampering performance
• Cutting-edge technologies enabling more cost-effective AI
• Cloud-like bare metal as a viable alternative to on-prem and cloud deployments
• Exclusive benchmark data demonstrating the efficiency of Intel^® compute technologies

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