Ctrl+Alt+Azure 308 - Running AI workloads at home
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Sep 17, 2025 Discover the ins and outs of setting up AI workloads at home, from laptops to custom rigs. Learn the benefits of keeping data local versus utilizing cloud services, touching on privacy and scalability. Explore budgeting strategies and hardware choices that can make or break your AI experience. Get insights into selecting the right large language models tailored for your specific applications, and see how platforms like Hugging Face can enhance your projects. Plus, enjoy a light-hearted discussion about office life and snorkeling adventures!
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Define The Full AI Workload Scope
- AI workloads span training, fine-tuning, inference, and supporting tasks like embeddings and RAG.
- Choose where to run them based on scale, cost, data sensitivity, and development stage.
Cloud Versus Local Tradeoffs
- Cloud gives scale, latest models, and pay-as-you-go convenience but can get costly.
- Local runs keep data private and lower recurring costs once hardware exists, but are hardware-limited.
Laptop-First Experimentation
- I mostly run local AI experiments on my laptop and use the cloud for heavy tasks.
- I haven't built a dedicated home rig yet because cloud covers 99% of my needs.