Run an LLM on Your Own Machine

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foundations

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• Choose between local LLM tools by your comfort and workflow, since they share one engine underneath
• Read VRAM and quantization to predict whether a given model will run on your machine
• Pick a local model by the job it has to do and check its license before committing
• Serve a local model to your own app through an OpenAI-compatible API by changing a single line of code
• Judge the trade-off between a local model and a frontier cloud model for each task
• Plan a hardware upgrade by sizing the right kind of memory to run a larger class of models

• Url: https://aiacademy.anthropos.work/chapters/local-llm-intro/ · Run an LLM on Your Own Machine: Start Here · Position: 1 · The map of this path — why running models locally is worth it, how the six chapters build on each other, and where to begin.
• Url: https://aiacademy.anthropos.work/chapters/local-llm-why/ · Why Run an LLM Locally · Position: 2 · The model on your own machine is weaker than the frontier — and that trade is worth making more often than you'd think.
• Url: https://aiacademy.anthropos.work/chapters/local-llm-tools/ · The Tools That Run Models · Position: 3 · Four friendly front doors, one shared engine underneath — so the choice is about comfort, not capability.
• Url: https://aiacademy.anthropos.work/chapters/local-llm-hardware/ · Hardware & Quantization Reality · Position: 4 · One number decides whether a model runs on your machine — and one trick lets you bend it.
• Url: https://aiacademy.anthropos.work/chapters/local-llm-pick-a-model/ · Pick Your Local Model · Position: 5 · Your hardware already narrowed the field — now choose by the job and read the license before you commit.
• Url: https://aiacademy.anthropos.work/chapters/local-llm-serving/ · Serve a Model to Your App · Position: 6 · Local models speak the same language as the cloud — so wiring one into your code is mostly changing a single line.
• Url: https://aiacademy.anthropos.work/chapters/local-llm-build-a-machine/ · Build a Better Home Machine · Position: 7 · Outgrew your hardware? The upgrade is one number — grow the right kind of memory, and a whole model class opens up.

• Choose between local LLM tools by your comfort and workflow, since they share one engine underneath
• Read VRAM and quantization to predict whether a given model will run on your machine
• Pick a local model by the job it has to do and check its license before committing
• Serve a local model to your own app through an OpenAI-compatible API by changing a single line of code
• Judge the trade-off between a local model and a frontier cloud model for each task
• Plan a hardware upgrade by sizing the right kind of memory to run a larger class of models

It is built for engineers and curious beginners who want a local LLM running on their own laptop or PC instead of relying only on cloud APIs. No prior experience with local models is assumed, since the path starts from why local matters and builds up step by step.

Course Mode: online · Course Workload: PT118M · Mode: online