Prime AI Compute
Where should your AI actually run?
Every AI project eventually hits the same practical question: does this workload belong on rented cloud hardware, or on machines you own? This page explains, in plain language and without vendor spin, what actually differs between classes of AI hardware, how the economics of owned versus rented compute really work, and the specific situations where running AI locally is the better decision. No benchmarks are invented here; the goal is to give you the reasoning framework, not a sales pitch.
What GPU classes actually differ in
A GPU (graphics processing unit) is the chip that does the heavy arithmetic behind modern AI. Marketing distinguishes dozens of models, but for AI work only three properties matter most:
Memory capacity
How much data the GPU can hold at once. A model must fit in GPU memory (called VRAM) to run at full speed. This single number decides which models a card can serve at all — capacity is a hard ceiling, not a performance knob.
Memory bandwidth
How fast the GPU can move data between its memory and its compute cores. Generating text is mostly a memory-movement problem, so bandwidth — not raw compute — usually sets the speed at which a model produces each word.
Interconnect
How fast multiple GPUs talk to each other. When a model is too large for one card, it is split across several, and the link between them — a standard PCI Express slot versus a dedicated high-speed GPU-to-GPU fabric — determines whether the group behaves like one big GPU or several slow ones.
Consumer gaming cards, workstation cards, and datacenter accelerators differ far more in these three dimensions than in raw compute. When you compare hardware, look these three properties up on the manufacturer's specification sheet for the exact model — they are public, checkable numbers, and they will tell you more than any headline benchmark.
The economics: owned hardware versus cloud inference
Inference means running a trained model to answer requests, as opposed to training, which builds the model in the first place. Cloud inference is metered: you pay per hour of rented GPU time or per unit of text processed. Owned hardware is the opposite shape: a large payment up front, then electricity and maintenance.
The variable that decides between them is utilization — what fraction of the time the hardware is actually doing work. A rented GPU that sits idle still bills by the hour, and an owned GPU that sits idle still cost its full purchase price. So the honest comparison is not "cloud price versus hardware price" but "what will my real usage pattern be":
- Bursty or experimental workloads favor the cloud. Renting for a spike and releasing it afterward beats owning hardware that idles the rest of the month.
- Steady, sustained workloads favor ownership. If a machine will run near-continuously for years, the up-front cost is spread across an enormous number of hours, and each hour becomes cheap.
- The crossover point is yours to compute. Take a real quote for the hardware you would buy, the metered price of the equivalent cloud instance, and an honest estimate of your monthly usage — the arithmetic is simple once the inputs are real, and misleading whenever they are guessed.
When local inference wins
"Local" here means hardware you control — a workstation, a home lab, or a server room — rather than a shared cloud. Three situations reliably favor it:
- Privacy. When prompts contain legal documents, medical records, or anything under a confidentiality obligation, local inference keeps the data on machines you physically control. No contract clause is as simple to reason about as data that never left the building.
- Latency. Latency is the delay between asking and receiving. A model on your own network skips the round trip to a distant datacenter, which matters for interactive tools, voice interfaces, and anything embedded in a real-time loop.
- Cost at volume. Per-request pricing is convenient at small scale and punishing at large scale. Once a workload runs constantly, owned hardware serving that same workload is the version of the utilization argument above where ownership clearly wins.
The cloud keeps the opposite advantages: instant access to the newest hardware, no maintenance burden, and elastic scale. Most serious setups end up hybrid — local machines for the private, constant, latency-sensitive core, and rented compute for spikes and experiments.
Part of a larger project
This site is one node in a network of small, honest educational sites built and run by one person, Joshua Lazoff, who operates his own AI hardware and writes from direct experience. Neighboring sites go deeper on adjacent questions: blackwellhost.com on current-generation GPU hosting, localaihosting.com on running models on your own machines, privateaicloud.com on private AI infrastructure, and digitaltwinpro.com on digital-twin work. The hub for the whole network — including the knowledge-graph and Torah-technology research it supports — is jew.tech.
Questions or corrections: joshua@digitaltwinpro.com
Last updated August 2026. A LazoffTech project.