No external inference
A local model may be necessary when document content cannot go to an external provider.
Guide / hardware before purchase
Most businesses should not start by buying a GPU.
A GPU becomes relevant when a local model, latency, concurrency, or no-external-inference requirement justifies it. The first question is what the workflow must answer and how many people will use it, not which card is fashionable.
Workload firstAPI is an optionHardware testedSupport included
Pattern demonstration / no hardware promise
When a GPU matters
Hardware follows the constraint.
A GPU can solve a real problem, but it also creates a real operating job.
More users or faster responses
Concurrency, context, batch processing, vision, or latency can make local acceleration useful.
Compare the full path
Hardware, power, cooling, maintenance, storage, model quality, and replacement belong beside API spend.
The wrong starting point
A GPU does not provide the workflow.
Choose what can run
Model size, quantization, context, and quality determine whether the hardware helps.
Prepare the sources
A fast model cannot compensate for poor documents, missing permissions, or a broken update path.
Plan the owner
Someone maintains the machine, runtime, model, backups, monitoring, and recovery path.
A hardware decision
Measure before you order.
Define the workflow
Name questions, documents, users, output, latency, concurrency, and privacy requirement.
Test an API or CPU path
Establish the simplest acceptable baseline before adding local inference.
Compare hardware
Evaluate memory, quantization, throughput, power, cost, support, and expected model quality.
Write the operating plan
Include updates, backups, monitoring, security, access, and a person who can repair the system.
A GPU is a deployment decision, not a strategy. The workload tells you whether you need one.
Why trust Pristine3D?
We build and operate production software.
Pristine3D Ltd builds and operates live digital products, and we run private AI workflows internally as part of our own operations. We scope around your real workflow: the documents you own, the questions your team asks, and the access boundary you approve. Based in Lagos, Nigeria, we work remotely with clients worldwide.
We already run live products
Based in Lagos, Nigeria, Pristine3D currently builds and operates smartcards.ng, venu.ng, photoshoot.ng, and ugc.ng in production.
We start with the actual workflow
One input, one output, one test set, and one person who owns the result. We scope a real workflow instead of a transformation programme.
The boundary stays visible
Cloud, model, storage, and messaging accounts stay in your name. The chosen data path, access rules, test record, documentation, and training are part of the agreed scope.
Straight answers
Common questions.
Can private AI run on CPU?
Some smaller models and workloads can. Quality, speed, context, and user count need testing.
Can you source the GPU?
Hardware can be scoped or coordinated after a measured workload, signed scope, and clear operating plan. We do not stock hardware speculatively.
Is a local model cheaper than an API?
It can be for some workloads, but total cost includes hardware, power, staff time, maintenance, and quality tradeoffs.
Own your knowledge base
The model is not the product. The knowledge base is.
Documents, the retrieval index, access rules, and the workflows built around them are the asset, and they compound. We deploy so the knowledge base stays yours: on your accounts, in the environment you choose, under access rules your team defines. The model behind the answers is a connector, so the knowledge base moves with you, not with a vendor.
Your corpus, your index
The document store, metadata, and retrieval setup live on accounts you own. No vendor holds the corpus.
Models are swappable parts
Change the model provider, move regions, or go local without rebuilding the knowledge base or the workflow.
The knowledge base is the alpha
Every improvement to the corpus improves the answers, and the improvement stays with you, not with a vendor.
Keep exploring
Related setups.
Start with the workload
What makes you think you need a GPU?
Tell us the users, model task, privacy requirement, latency, and support capacity. We will test the need before sizing hardware.