Why the name, why Troy, who is behind it.
The Capital Region has a research university, a semiconductor corridor and a lot of people who need GPUs. What it does not have is anywhere local to get them. That gap is the whole idea.
A catalyst doesn’t do
the reaction for you.
It lowers the activation energy, so a reaction that was impossible at your temperature becomes routine. The reactants were always capable of becoming the product. The barrier was the problem.
That is a fair description of what a 96 GB card does for a student project. The idea was fine. The maths was fine. What stopped it was twenty-four gigabytes of VRAM and a Colab session that dies after twelve hours. Remove that and the same person gets the same result, just without the barrier in the way.
The other half of the metaphor is the hardware itself: a catalytic converter is a honeycomb monolith with a few grams of platinum on it, and it is the single most quietly impressive piece of engineering on an ordinary car. Small, unglamorous, does one job extremely well. We would settle for that.
Small, local,
and specific.
We are not competing with a hyperscaler on scale, and there is no point pretending otherwise. Two nodes is two nodes.
What two nodes in Troy can do that a region in Virginia cannot: pick up the phone, sit down with you and work out what you actually need, keep your data somewhere you could drive to, and charge a student something a student can pay. For a grad researcher against a thesis deadline, or a shop that has never provisioned a GPU before, that usually matters more than elastic capacity.
We build our own things too — ToxScreen, Lithic, the Naxi translation models — which means when we say a machine suits a job, it is because we have run that job on it.
Founder-led, with roles we are hiring into.
Kevin Peters
Rising senior at RPI, deep in the AI/ML community on campus and connected across student researchers, faculty and clubs. Runs client relationships, campus outreach and product direction.
ML engineer
Owns the fine-tuning pipelines, model evaluation, dataset curation and inference optimisation. The person who turns somebody’s data into a model that actually works.
Systems engineer
Owns the hardware: builds, networking, driver stacks, containers, monitoring and on-premise deployments. The person who keeps the nodes up.
Community lead
Runs the campus presence — posters, club talks, the Discord, faculty outreach. The person who makes sure everyone at RPI who needs a GPU knows we exist.
Client engineer
Works directly with local businesses and research groups: understands the need, scopes the project, sees it through. Technical enough to spec a server, commercial enough to close.
Interested in one of these roles?
We would rather hear from someone unusual than fill a seat.
Whose work this stands on.
Our products apply published methods rather than inventing new ones. It seems worth being explicit about whose work we are standing on.
ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries
MoleculeNet: a benchmark for molecular machine learning
DeepCAD: a deep generative network for computer-aided design models
Text2CAD: generating sequential CAD models from text descriptions
QLoRA: efficient finetuning of quantized language models
FlashAttention: fast and memory-efficient exact attention with IO-awareness
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Megatron-LM: training multi-billion parameter language models using model parallelism
Come and see the rack.
If you are in the Capital Region and curious, we would honestly rather show you the machines than write another paragraph about them.