Independent AI infrastructure research
Compute choices, examined clearly.
Practical analysis of personal AI supercomputers, local LLM hardware, GPU infrastructure, and cloud computing for developers and businesses.
Current research
Three decisions facing local AI teams now
Specification analysis and buyer guidance based on official sources—never invented benchmarks or testing claims.
NVIDIA DGX Spark: Who Is It Actually For?
A specification-led buyer's guide to DGX Spark, including the workloads it suits, the constraints buyers should verify, and the alternatives worth considering.
02 / ANALYSISDGX Spark vs Cloud GPUs: When Does Buying Hardware Make Sense?
A transparent cost and operations framework for deciding between DGX Spark and rented cloud GPUs without pretending unlike accelerators are performance-equivalent.
03 / ANALYSISDGX Spark vs Mac Studio for Local AI Workloads
A workload-first comparison of DGX Spark and the announced M5-generation Mac Studio, covering memory, software ecosystems, local inference, and buyer fit.
How we work
Research built for decisions, not pageviews.
- Primary sources before summaries
Manufacturer documentation and official pricing pages anchor material claims.
- Estimates remain estimates
Cost models expose their inputs and avoid presenting arithmetic as measured performance.
- Fit over universal rankings
Hardware and cloud recommendations are tied to workload, team, and operational constraints.