AI
Local inference and AI infrastructure.
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[Local LLM 05] Tokens, Context, Quantization and Memory Use
Understand how token count, context length and weight quantization affect local model memory requirements.
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[Local LLM 06] Move Model Storage and Check Offline Operation
Find the model storage path, move it with the relevant runtime settings, and check what remains available without a network connection.
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[Local LLM 07] Use GGUF Models and the API in LM Studio
Load a compatible GGUF model in LM Studio, inspect resource settings and test its local API with the selected model.
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[Local LLM 08] Install Python and Create Project Environments
Install Python, create a virtual environment per project and verify which interpreter and package installer are active.
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[Local LLM 09] Install Node.js and npm and Check Executable Paths
Verify Node.js and npm installation, locate the executable used by the terminal, and inspect project dependency handling.
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[Local LLM 10] Install WSL2 and Understand Files and Networking
Prepare WSL2 and identify which environment owns a file path, process or network endpoint when working between Windows and Linux.
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[Local LLM 11] Understand Docker Desktop, Containers, Volumes and Ports
Install and check Docker Desktop, then distinguish container processes, persistent volumes and published ports.
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[Local LLM 12] Set Up Web Chat with Open WebUI and Ollama
Connect Open WebUI to the correct Ollama endpoint and verify model discovery, container networking and persistent data.
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[Local LLM 13] Call the Ollama API from Python
Send a request to the local Ollama API from Python, handle the response, and distinguish connection errors from model errors.