2100
Median estimated cost reduction
For Boltz-1 at 0.4 LDDT, our scaling-law analysis estimates 2100× lower training cost at a fixed training time.
Read the researchKuai
We train models orders-of-magnitude cheaper.
Plug-and-play
We integrate as a drop-in layer for your model, dataset, and hardware, so you keep control of your data and infrastructure.
General by design
Training efficiency know-how is concentrated in a small number of labs. We package that expertise into a system that transfers across domains and model families.
Carefully quantified
We rigorously quantify uncertainty in every step of our algorithms and empirical estimates to ensure our methods are reliable.
Efficiency is designed.
Algorithms are the bottleneck
Compute is widely available. What’s scarce is algorithmic innovation that reliably improves training efficiency across model classes and domains.
Embarrassingly parallel by default
Instead of sharding one monolithic run, we train multiple models on distilled versions of the data, then ensemble, minimizing coordination overhead.
Built with practitioners
We co-design with teams training large, custom models, staying grounded in real constraints and iterating quickly from deployment feedback.
We’re a research lab, not a static algorithm
We continually develop and validate new training strategies as architectures, datasets, and hardware evolve. Kuai improves over time.
About us.
Who we are
We’re a small team of MIT PhD researchers building Kuai to bring frontier-grade training efficiency to far more teams.
Researchers and builders
We’ve spent years studying training dynamics and scaling behavior, and we build systems meant to run end-to-end in real training pipelines.
What we care about
We ground decisions in strong empirical evidence and explicit theoretical assumptions. Data alone is rarely sufficient: inductive biases matter, and we are deliberate about choosing them.
Get in touch
If training cost is limiting the models or experiments your team can attempt, we’d love to talk.