Buying guide
A private AI computer under $1,000
A sourced shortlist that starts with memory, uses complete-system prices, and makes room for uncertainty.
Read the guide →Practical field guides
Make the next decision a clearer one.
Start with the workload. Understand the memory. Check the sources before buying.
Buying guide
A private AI computer under $2,000
A sourced shortlist that starts with memory, uses complete-system prices, and makes room for uncertainty.
Read the guide →Buying guide
A private AI computer under $4,000
A sourced shortlist that starts with memory, uses complete-system prices, and makes room for uncertainty.
Read the guide →Platform guide
Mac or PC for local AI?
Choose a memory architecture and software ecosystem that match your workload.
Read the guide →Memory planning
How much memory does a 70B model need?
Start with the actual files. Then add the context cache and runtime overhead.
Read the guide →Platform comparison
DGX Spark, Mac Studio, or Ryzen AI Max?
A source-led comparison of capacity, bandwidth, price evidence, and software requirements.
Read the guide →Privacy fundamentals
What “private AI” really means
Local inference is a useful property. The whole application still determines where data goes.
Read the guide →Scaling guide
When does a private AI setup need a cluster?
Start with a capacity or concurrency constraint you can measure.
Read the guide →Model fundamentals
Choose a quantization with room to spare
Compare actual files, then weigh memory savings against output quality for your task.
Read the guide →Memory planning
Context length is a memory budget
Understand the cache that grows with your conversation or document.
Read the guide →