SLMCustom AI machines: build for your workload, not a headline GPU count
How to specify a local AI workstation for response speed, larger models, concurrent users and predictable everyday operation.
A custom AI machine starts with a performance brief: the work it must complete, the quality it must achieve and the time users can wait. GPU count and memory capacity are design inputs, not a performance guarantee.
Choose the result you want to improve
A researcher may need reliable answers from long papers. An internal service may need several concurrent requests. A salesperson may want short preparation notes before a call. These tasks stress different parts of a system. Decide whether your priority is response delay, total daily throughput, model capacity or running several tools together.
Separate memory types
NVIDIA’s current local-AI guide distinguishes GPU-memory workstations from unified-memory systems such as DGX Spark. Those memory figures describe different architectures. Compare a complete workload on each candidate rather than treating a memory total as an interchangeable measure of speed.
Check the full system
Model weights are only part of memory use. Context, concurrent sessions, document retrieval and supporting tools add overhead. Storage affects document ingestion and model loading. Power delivery, cooling, physical space and noise affect whether the machine suits the room where it will run. Include expected operating hours in the brief.
Multiple GPUs need a software plan
Some workloads split a model across devices; others run separate jobs on separate GPUs. The runtime, model and interconnect determine what is possible. Memory is not automatically pooled into one large card. Ask for a test of the intended layout and record the exact serving configuration.
Accept the machine against a repeatable test
Use your own representative examples. Record answer quality, source accuracy, time to first output, completion time and behaviour under simultaneous requests. Re-run the agreed test after model or runtime changes. Include a long-running test if the machine will operate continuously. Keep results together with software versions and configuration.
Make the quote specific
SLM Pros Custom configurations start from the proposed tier shown on the machines page, with final parts and integration confirmed for the project. Ask for separate hardware, setup, maintenance and ongoing support costs. A performance target should be attached to a defined workload and demonstrated configuration. New orders are fulfilled from 2027 onward.
Does a custom build always need several GPUs?
No. A single suitable GPU may meet the task more simply. Add devices when a tested capacity, concurrency or isolation requirement justifies them.
Sources & further reading
Reviewed 4 November 2026. Vendor specifications and community reports are attributed to their sources. Project performance is assessed separately.