AI Gear Stack is an independent publication about the hardware that powers local AI, AI development, homelabs and modern developer workflows.
We exist because the question most hardware coverage answers — which product has the highest numbers? — is almost never the question a buyer actually has. The real question is narrower and much more useful:
Which product is the right choice for this workload, this budget and these constraints?
Why this site exists
Running AI models on your own hardware went from a niche hobby to a mainstream engineering decision in the space of about two years. The buying advice did not keep up.
Search for a GPU recommendation and you will mostly find gaming benchmarks. Frame rates tell you almost nothing about whether a card can hold a 32-billion-parameter model in memory, or how many tokens per second it will produce once it does. Those are different questions with different answers, and getting them wrong is an expensive mistake.
The same gap runs through the whole stack. Mini PC reviews measure boot times and rarely mention memory bandwidth — the single number that most determines local inference speed. NAS coverage discusses media streaming, not dataset throughput. Monitor reviews chase colour accuracy for photographers when the reader is a developer who wants sharp text and a working KVM.
We write for the person on the other side of that gap.
How we approach hardware
Three principles shape everything here.
Workload first, specification second. A specification only matters in the context of something you are trying to do. 16 GB of VRAM is generous for Stable Diffusion and limiting for a 32B language model. We start from what you want to run and work backwards.
Capacity determines what runs; bandwidth determines how fast. For local AI this is the single most useful mental model, and it explains most of the counter-intuitive results in the field — including why a mini PC with 128 GB of slow unified memory can run models that a far more expensive graphics card cannot load at all.
Say what we do not know. Every review and guide on this site states plainly what the assessment was based on: manufacturer specifications, independently published benchmark data, or hardware we tested ourselves. These are not the same thing and we do not blur them. If we have not put our hands on a machine, we say so, on the page, every time.
How we pay for it
AI Gear Stack earns affiliate commission when readers buy through links on the site. This is disclosed on every page that carries a commercial link, and it does not influence what we recommend — our editorial policy sets out exactly how that separation is maintained.
We do not accept payment for coverage, for a recommendation, or for a score.
What you will find here
Eight hardware pillars, each with buying guides, reviews, comparisons and the explainers that make them make sense:
- AI Workstations — complete systems, chosen by what they can actually run
- GPUs — graphics cards evaluated for AI work rather than gaming
- Mini PCs — small machines for homelabs, inference nodes and quiet desks
- Local AI — requirements, memory maths and capability guides
- Homelab — servers, racks, power and supporting infrastructure
- Storage — SSDs and NAS sized for weights, checkpoints and datasets
- Networking — moving large files without waiting on gigabit
- Developer Gear — monitors, keyboards and docks for people who live in an editor
Corrections
Hardware moves quickly and we get things wrong. When we do, we fix it and say so — see our corrections policy. If you have spotted an error, please tell us.