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Self-Hosted LLMs in Practice

What Does “Local” Actually Solve?
  • ○ From Model to Inference System
  • ○ Local, Hosted, or Hybrid?
  • ○ Define the Workload Before Choosing a Model
Model Choice, Memory, and Hardware
  • ○ Choose for the Task, Not the Leaderboard
  • ○ Understand Memory Without One Universal Number
  • ○ Match Model Size and Context to the Task
Model Files, Quantization, and Licensing
  • ○ Weights, Formats, and the Runtime Are Different Things
  • ○ Quantization Is a Trade-Off, Not Free Compression
  • ○ Treat Model Licenses as a Deployment Requirement
Running a Local Model Without Writing Software
  • ○ The First Local Run
  • ○ From a Chat Window to a Service
  • ○ Choose a Serving Path by Requirement
Evaluation, Privacy, and the Deployment Decision
  • ○ Evaluate Output Quality on Your Task
  • ○ Measure Speed in a Way That Can Be Repeated
  • ○ “Local” Is Not a Security Control by Itself
  • ○ Decide: Local, Hosted, Hybrid, or Stop

Running a Local Model Without Writing Software

Author

  • Rida Berezova

You can learn a lot without building an API client. This module covers a first desktop run, the jump from a chat window to a service boundary, and how to choose a serving path by requirement — desktop exploration, flexible local formats, or GPU-oriented multi-user serving.

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Topics

  • The First Local Run
  • From a Chat Window to a Service
  • Choose a Serving Path by Requirement
Privacy