It depends on the model, quantisation, context length and concurrency. A requirements assessment is more reliable than a blanket GB figure.
Local AI infrastructure
AI server hardware: select it around the workload, not the data sheet
The question is not only which GPU is available. The model, prompt and response lengths, expected responsiveness and simultaneous requests all matter.
Technical prerequisites
What is clarified before starting
- A workload profile for chat, document search or API use
- An estimate of concurrent users and expected context sizes
- Constraints for budget, noise, power and expandability
Typical use cases
Where this architecture is used
- Procuring a first local AI platform
- Assessing existing workstations or servers
- Expanding a successful pilot into team operations
At a glance
Technical context without false precision
| Memory | model weights, context and parallel requests require headroom |
|---|---|
| GPU | relevant for acceleration and potentially multi-user operation |
| Operations | allow for cooling, power, drivers, support and maintenance windows |
Scope and limitations
What this solution intentionally does not promise
Technical fit is checked before ordering. Vendor claims and isolated benchmarks do not replace tests with the intended model and your real workload.
FAQ
Frequently asked questions
Potentially, if operating system, drivers, memory and actual workload fit. We assess this before implementation.
Sources and technical context
These sources support technical context. They are not a project commitment and do not replace individual assessment.