MEM
Memory capacity
The first filter. The model's weights, plus working memory for every active conversation, must fit. Quantization (storing weights at lower precision) shrinks the first part; it doesn't remove the second.
CSTM-SRC · REV AcstmAI™ · Hardware · Sourcing
Vendor-neutral AI hardware, sourced on the merits.
We spec from multiple vendors, with no lock-in. Every GPU, server, switch and drive on a cstmAI configuration sheet is there because it suits your workload, can be delivered, and can be supported.
Fig. 01 · Pattern storage above the shop floor, Paterson, NJ
Selection criteria
Seven questions for every part.
Each configuration sheet answers these in writing, so you can check our reasoning and reuse it for your next purchase.
| Workload fit | Measured on your task: quality, response speed and concurrency on candidate hardware. |
|---|---|
| Memory | Enough GPU memory for the model, its context and the number of simultaneous users. |
| Availability | Real lead times from distribution, not list availability. A part you can't get is the wrong part. |
| Power & cooling | What your room or colocation cage can actually supply and remove, continuously. |
| Support | Warranty terms, parts availability and the manufacturer's repair process. |
| Software support | Mature drivers and runtime support for the models and serving software you'll use. |
| Exit cost | Nothing proprietary that would tie your next purchase to one vendor, or to us. |
Choosing GPUs
What actually decides the GPU.
Model names and launch benchmarks get the attention. These six factors decide whether a GPU suits your workload.
BW
Memory bandwidth
How fast the GPU can read those weights. For serving language models it often matters more than raw compute, because each generated word reads the model again.
LINK
Interconnect
When a model spans several GPUs, the links between them set the ceiling. High-bandwidth GPU-to-GPU links matter for large models and training; less so for many small ones.
FMT
Precision support
Newer GPUs run lower-precision number formats natively, which can raise throughput substantially. We check the model and serving software support them before counting on it.
PWR
Power and heat
Data-center GPUs draw several hundred watts each, some approaching a kilowatt. That decides circuits, cooling and whether a room works at all.
SUP
Supply
Lead times swing from days to months by part and quarter. We price alternatives so a shortage changes the schedule, not the project.
Reel 02 · Machine shop interior, Paterson, NJca. 1890
FAQ
Straight answers on sourcing.
Do you have partnerships with hardware vendors?
Our recommendations aren't tied to any manufacturer. If a commercial relationship ever bears on a quote, we'll tell you in the quote.
NVIDIA or AMD?
Whichever fits. NVIDIA has the broadest software support today; AMD's data-center GPUs offer large memory and are well supported by mainstream serving software. We test your model on the software stack we'd ship before recommending either.
Should we wait for the next GPU generation?
Rarely. A new generation takes months to reach volume supply and mature software. If your workload is ready now, a system sized for it now usually pays back sooner; we plan the refresh at the same time.
Get a quote
Spec your system.
Tell us the models you want to run, how many people will use them and where the hardware should live. An engineer replies with a first configuration and the questions that decide the quote.