01 GPU / TENSOR CORE 01 / 07
Local AI & implementation
AI that runs on hardware you own, tuned to the work you actually do.
Most AI advice starts with a product. This starts with your work. We look at the tasks worth handing off, then decide what should run locally on your own hardware and what is better served by an API. You keep the data, the model weights, and the off switch.
What you get
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01
A model that fits your machine
Model and quantization chosen against the GPU, memory and context length you actually have, with measured tokens per second instead of a guess.
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02
A private endpoint
An OpenAI compatible server on your own network, reachable from the tools you already use, with keys you control and nothing leaving the building.
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03
Your documents, searchable
Retrieval over your own files with citations back to the source page, so an answer can be checked instead of trusted.
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04
A written handover
How it starts, how to update it, what it costs to run, and the specific things it will get wrong.
How it works
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01
Inventory
We list the tasks, the data they touch and the hardware on hand. Some of it will not be worth automating, and we say so.
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02
Bench
Two or three candidate models run against your real prompts. Quality, speed and memory compared side by side, then a choice made with the numbers in view.
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03
Install
The server, the interface and the access rules go in, with autostart, logging and a way to roll back.
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04
Handover
You run it for a week while we are still around to adjust it.
Good fit if
- Your data cannot go to somebody else’s API.
- You have a workstation or a server with a capable GPU and nothing useful running on it.
- You tried a hosted assistant and it knew nothing about your business.
NEXT STEP
Let’s talk it through.
Thirty minutes, no pitch deck. Bring the idea, the deadline, or the thing that keeps breaking.