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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.
5 articles
What a token costs: five routes, the math, and the break-even point
Frontier API, subscription, inference providers, rented GPU or your own hardware: five routes to tokens, with real monthly costs, the electricity bill, and the calculated break-even point.
NVIDIA, AMD and Intel for local AI: which platform to choose
CUDA, ROCm or Vulkan? We compare the three local-AI platforms based on what is actually available: official software stacks, cards by VRAM tier (12, 16 and 24GB) and real speeds. Where the obvious choice remains, where AMD wins on value, and when it makes sense to leave NVIDIA.
Qwen3.8-27B vs Gemma 4 26B A4B: the new frontier at 24GB
A 27.8B dense or a MoE with 3.8B active, a 262K context against 256K and almost the same weight at Q4_K_M: two opposite ways of putting big-model quality on a 24GB consumer card. Which is faster, which goes deeper, and when to pick one over the other.
Ollama vs llama.cpp: under the hood
Ollama runs llama.cpp under the hood, but the two are not the same thing: one is the engine, the other the car. What's really underneath, when the difference matters, and how to choose without slogans.
Ollama vs LM Studio: Which Tool to Choose
Ollama for servers and scripts, LM Studio for desktop exploration: the comparison table, the two APIs (11434/1234) and the real case of using both.