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Articles, guides, and insights on local artificial intelligence
Reading paths
Start here
The in-order path for newcomers to local AI: from basic concepts to installation and your first model.
Optimize performance
Pick the right hardware and get the most out of what you already own: CPU vs GPU, Mac, Raspberry Pi, and fast serving.
Data and privacy
What actually goes over the network with local AI: the data, the security, the compliance, and the control that stays in your hands.
Choosing hardware and model
How to pick the right hardware and the model suited to your use: VRAM, quantization, GPU or CPU and response speed.
2 articles
Mac Studio M5 Ultra: practical 512GB guide - models that fit and GGUF setup
What actually fits in the 512GB Mac Studio M5 Ultra: the 4-bit rule for MoE (active vs total params), three GGUF setup paths (Ollama, LM Studio, llama.cpp), why the KV cache is the real bottleneck, and when the $9,999 pays back against APIs.
vLLM: serving models fast
vLLM is the open-source engine that turns a local model into a concurrent server: PagedAttention, continuous batching, installation and the key concepts for choosing when to use it instead of Ollama.