Local GPU for the teams evaluating private AI first.
AI workstations put a capable GPU under a desk or in a lab: enough to run small and mid-sized open-weight models with Ollama, test RAG over a sample collection, and prove a use case before a server purchase.
What a workstation is good for.
Model evaluation
Compare candidate open-weight models on your own prompts before sizing servers.
Pilots
Run a governed pilot for a small team with real data that cannot go to a hosted API.
Development
Give engineers a local OpenAI-compatible endpoint for building against private models.
Isolated analysis
Analysts in sensitive programs can work with AI on a machine that never connects out.
Where the line usually falls.
| Need | Workstation | GPU server |
|---|---|---|
| Users | One person or a small team | Departments and applications |
| Models | Small to mid-sized, often quantized | Mid-sized to large, multi-GPU |
| Runtime | Ollama | vLLM with batching |
| Availability | Single machine | Redundant nodes possible |
| Placement | Desk, lab, or office | Data center rack |
A pilot should produce evidence, not just a demo.
Workstations can run Data.co Control in a single-node configuration, so pilot users authenticate, policies apply, and every request is logged. When the pilot ends, you have usage data and audit records to justify the next step, and policies that carry forward unchanged to production servers.
Sizing is simpler than for servers but follows the same logic: GPU memory determines which models fit and at what precision, and context length and concurrency use what is left.
Before choosing a workstation.
- Largest model you want to test
- Acceptable quantization level
- Number of simultaneous users
- Office power, noise, and heat
- Physical security of the location
- Whether it will ever connect to a network
Where workstation specifications actually differ.
GPU memory
The deciding number. It sets the largest model and precision you can test; compute speed matters less for a single user than whether the model fits at all.
Unified memory systems
Apple silicon and similar designs share memory between CPU and GPU, which lets larger quantized models load, generally with lower throughput than a discrete data-center GPU.
System RAM and storage
Plan for several model files at once during evaluation, plus a sample document collection and its index. Fast local storage shortens model swaps.
Thermals and noise
A GPU under sustained load runs hot and loud. An office corner is different from a lab bench or a small server closet.
Turning a workstation into evidence for a decision.
- 1
Pick one use case
Choose a narrow, real task with a clear owner, such as summarizing a specific document type for one team.
- 2
Write the success test
Collect example inputs and what a good output looks like before users start, so results are judged against something.
- 3
Run it governed
Users sign in through your identity provider, requests pass the gateway, and every decision is logged from the first day.
- 4
Decide with data
Review quality against the test set, usage patterns, and denied requests, then size production or stop.
Desk-side machines need desk-side controls.
- Full-disk encryption on the system and data drives
- Sign-in through managed accounts, not shared local logins
- Removable media policy that matches the data on the machine
- Physical lock or a room with controlled access
- Patch management enrolled like any other managed endpoint
- A disposal plan for drives that held sensitive documents
Questions buyers ask
Can a workstation run a large model?
Larger models can sometimes run on a workstation with aggressive quantization, at reduced speed and potentially reduced quality. For production use with large models, GPU servers are usually the better fit.
Do workstations run the full Data.co Control?
They can run a single-node configuration with the gateway, policy engine, RAG, and audit log, which is suited to pilots and small teams.
Can a workstation work fully offline?
Yes. With offline licensing and models loaded locally, a workstation can operate with no network connection.
What happens to the workstation after the pilot?
Many teams keep them for model evaluation and development, while production moves to servers. Policies and registry entries carry forward.
How many users can share one workstation?
A handful of light users can share a well-specified machine, especially with small models. Ollama serves requests with less batching than vLLM, so response times rise quickly as simultaneous users grow; that is usually the signal to move to a server.
Can developers use a workstation as their local AI endpoint?
Yes. Through the gateway it exposes an OpenAI-compatible API, so code written against it moves to production servers by changing the base URL, with the same aliases and policies.
Size it before you buy it.
An infrastructure assessment maps your workloads, data constraints, and existing hardware to a deployment model and a bill of materials you can review.