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Deep Dive into Premio’s On-Prem Edge AI Servers

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Deep Dive into Premio’s On-Prem Edge AI Servers

Deep Dive into Premio’s On-Prem Edge AI Servers

Your Guide to Understanding & Configuring the Right Industrial Computer

Speaker: Jazlyn Ho

In this webinar, you’ll learn how to navigate the shift from cloud to on-prem LLM deployments and see how Premio’s On-Prem LLM Series are purpose-built to power this transition.

Key concepts:

  • Emerging market trends accelerating edge AI adoption
  • Core challenges of running LLMs exclusively in the cloud
  • Insights on where the LLM Series is positioned in the Edge Continuum
  • A comprehensive overview of the LLM Series

Designed for the On-Prem Data Center Edge

LLM-1U-RPL 1U Edge AI Rackmount Server with 12th/13th Gen Intel® Core® Processor and Q670E Chipset

  • Short-Depth 1U Form Factor
  • Intel® Core™ E Processors
  • Dedicated GPU Acceleration
  • Comprehensive IoT Connectivity
  • Multi-Layer Hardware Tamper-Proof Security
  • Power Supply & Smart Fan Redundancy
  • World-Class Safety Certifications (UL, FCC, CE)

Key Features

Optimized Processor for Edge AI

Leverages Intel’s performance hybrid architecture with 10-year lifecycle support to streamline intensive industrial workloads.

GPU Support for Gen AI Acceleration

Supports workstation-class GPUs that enable high-performance inferencing for on-prem LLM and genAI.

Hardware Level Cybersecurity

Features multiple hardware-level protection for physical security, anti-tampering, and data integrity.

Get Guidance on Navigating Your On-Prem Edge AI Server

Technical Blogs

View a side-by-side comparison between Edge LLMs and Cloud LLMs to select the right edge AI solution for your next deployment.

Explore how edge servers are driving the latest edge and generative AI technologies on-premise for Industry 4.0 deployment!

Explore how edge servers enable real-time, secure, and scalable deployment of on-premise LLM and generative AI workloads by processing data locally within hybrid cloud architectures.

Dive deep into the definition, use case, challenges, and how edge computing supports Physical AI.