Benches the Firm Owns Outright
Validation runs on RTX Pro class benches the firm owns outright. Everything the method needs, the firm owns - nothing depends on third-party hardware or a rented cloud.
AI Tools
GPU Lab
The GPU lab is where your hardware decision becomes an empirical measurement. We benchmark real models on real hardware under your actual workload, so the stack you buy is the stack the evidence proved.
What the lab establishes
Six things the lab proves before any hardware is procured.
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Your Real Workload, Never a Generic Benchmark
In our GPU lab first, then deployed to your own hardware. The On-Site AI Architect carries the build into the room where it will run. Latency, throughput, and memory are measured under your actual load, never a generic benchmark.
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Validated Where It Will Run
Validation happens on the real in-network infrastructure the system will run on in production. It does not use a sandbox, a rented cloud account, or a slide deck.
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Set Aside Before Consuming Capital
Sizing by optimism. What fails validation is set aside before consuming capital. Procurement buys nothing more than what the validated prototype proved necessary.
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Sizing Ranges, Never a Price
Three governing inputs - model class, concurrent load, max context - mapped to hardware stack. The ranges are methodology, never a price. They carry the dimension each gate or decision is measured against.
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Thoroughly Documented, Fully Yours
Every configuration and optimization decision is thoroughly documented and handed to your team. You retain full ownership when upgrading or swapping models.
Where the proof lives
The validation run is the lab's proof artifact. Its steps, measurements, and flagged exceptions render below exactly as the lab records them.
Each validation run measures how much the hardware configuration can carry, how fast the model answers, and how well it does the work - all on your own workload. The results are reference points for a procurement decision, not promises:
- Maximum concurrency. How many requests the hardware configuration sustains at the same time before response times start to slip.
- Reference times for inference. The measured response times of the model on your workload, taken from the recorded run.
- Speed in tokens per second. A token is a small piece of text, roughly a word or part of one. Tokens per second states how fast the model produces its answer.
- The language you work in. The model's answers have to hold up in the language your team reads and writes every day.
- Professional terminology. The model's answers have to use the terms of your field correctly.
- Cooperation with agents. The model has to keep this quality as part of an agent setup, handing work to the right tool or the right person.
Where the lab's work continues
The GPU lab is one part of a Private Vector delivery, not the delivery itself. The pages below carry the rest: the stage that runs the validation, the capability the lab sizes, and the guidance the sizing ranges feed.