Live Webinar

Shared vs Private LLMs: Why Real-World AI Needs More than a Public Endpoint

Unlock Faster Latency, Lower Costs, and Full Control with a Dedicated LLM Stack

Open-source LLMs give you freedom — until shared infrastructure takes it away.

Join our live session on May 30 at 10 AM PT for a deep-dive into why shared LLMs fail at scale, and how to move to a private, high-performance, cost-efficient setup without complexity.

What you’ll learn:

  • The Shared Model Paradox: Why latency spikes, rate limits, and vendor control hurt UX

  • Privacy-First LLM Deployments: How to keep prompts, data, and weights in your boundary

  • TCO Benchmarks That Matter: Real cost comparisons across public endpoints, dedicated GPUs, and smart autoscaling

  • Turbocharged Performance: Benchmarks on how private LLMs beat shared endpoints in speed and reliability

  • Live Demo: Deploy your own private LLM endpoint in minutes — no PhD in infra required

Whether you're tuning response times, chasing better unit economics, or navigating enterprise compliance you will leave with actionable blueprints and clear trade-off frameworks for building the LLM serving architecture you actually need.

Featured Speakers:

Joseph Baker

Joseph Barker

Principal Engineer
Linkedin
Jeff K square

Jeffery Kinnison

Machine Learning Engineer
Linkedin

Ready to efficiently fine-tune and serve your own LLM?