As enterprise AI moves from experimentation to production, organizations need a practical approach to where workloads run and how infrastructure is sized. In this episode of On Record, Manta Rastogi explores enterprise AI adoption trends, the challenges of on-premises inferencing, and how to align infrastructure with model requirements, performance goals, and business needs.
Learn how to approach the model-sizing problem by considering factors such as model size, precision, context length, input and output sequence length, concurrency, and latency requirements. The discussion also highlights the Dell PowerEdge portfolio for AI, including systems designed for inference optimization, fine-tuning, and more demanding enterprise AI workloads.
Explore practical guidance for building a right-sized AI environment and using performance comparisons to inform infrastructure decisions for enterprise inferencing.
#DellTechnologies #EnterpriseAI #AIInferencing
Subscribe here ► https://youtube.com/delltechnologies
Follow us on:
LinkedIn: https://www.linkedin.com/company/delltechnologies
X: https://x.com/dell
Instagram: https://www.instagram.com/dell/
Threads: https://www.threads.com/@delltech
TikTok: https://www.tiktok.com/@dell
