Why We Built NERDIT
The story behind NERDIT and why local AI infrastructure is the next frontier.
The problem we kept running into
Every ML team we talked to had the same story: powerful GPUs sitting idle, researchers waiting hours for jobs to run, and cloud bills that made finance teams nervous.
The hardware was there. The talent was there. But the tooling was missing.
The GPU paradox
Here's the paradox: GPUs are the most powerful compute hardware ever built for ML workloads, yet the average utilization across research labs and companies hovers around 40%. Not because the work isn't there — but because orchestrating that hardware is painful.
Setting up CUDA, managing Python environments, handling multi-node jobs, recovering from crashes — each of these is a solved problem in isolation. But stringing them together into something that just works? That's where teams lose days.
What we set out to build
We wanted something that would let a researcher type:
nerdit run train.py
And have everything else handled automatically. GPU detection, environment setup, job queuing, crash recovery, monitoring — all invisible.
Where we are today
NERDIT is in beta. We're working with early teams to validate our approach and sharpen the product. If you're running GPU workloads locally — or wishing you could — we'd love to hear from you.
Join the beta and help us build the right thing.