How IOTA works
IOTA coordinates compute that is scattered, mixed, and always changing, unifying it as one system you can use.
The principle
Conventional training assumes one block of matched, reserved hardware, held without a break for the whole run. That single assumption is what puts most of the world's compute out of reach. IOTA is built the opposite way.
Conventional training needs one large block of matched, reserved hardware, held without a break for the length of the run. iota removes that requirement, coordinating the work across many distributed machines, keeping the run going even as they join and leave.
How the mechanism creates value
The same disaggregated training works on all three constraints. Here is what it changes for each.
Everything below comes from the single mechanism above, pointed at a different constraint.
Training economics effect
A run no longer needs owned or reserved hardware, so the cost of training comes down. More models become affordable to build, and you iterate more often for the same spend.
Capacity effect
The work can use compute conventional training cannot, so more usable capacity comes within reach. You start what you were waiting on, instead of holding out for a cluster to free up.
Platform economics effect
Scattered, variable capacity can be put to work, so more of an existing fleet stays productive. Utilisation rises, and you serve more workloads without buying more hardware.
HOW THE PLATFORM WORKS
iota works the same way underneath, whatever you came here to solve. Pick the lens that fits you, and follow how it works from there.
Active platform function
You define the model, the data, and the training objective. iota prepares that workload to run across many separate machines, so nothing has to be rebuilt for the compute underneath it.
Same mechanism, three ways to see it. Whichever lens you pick, iota is doing the one thing underneath: turning compute you could not use into training that runs.
Each stage hands cleanly to the next, so the work moves from start to result without you assembling or managing the pieces underneath.
What IOTA handles underneath
Coordinating work across scattered, variable compute is difficult. IOTA takes on the parts that make it hold together, so the system stays steady without you managing it.
Underlying capability
We get a model, dataset, and objective ready to run across many machines, not just one. That means splitting the work so it can be spread out, and setting it up so every machine knows its part from the first step.
Where that matters
The mechanism is the same for everyone. What it unlocks depends on who you are and what you are trying to do.
Train the models your budget and cluster access would rule out, and iterate more often for the same spend.
Run training on IOTA →Turn capacity you could not use before into training you can offer, serve more workloads, and reach the customers your cost base used to price out.
Explore platform use →Run demanding, experimental training on flexible capacity, without a reserved cluster sitting between you and the work.
Use IOTA →Next step
Start building yourself, or bring us the workload and we will shape it with you.
SDK
The self-directed route. Integrate IOTA and run your own workloads, with the disaggregated training handled underneath.
Train with IOTA
The managed route. Bring a defined workload, and we take it from qualification through to a result you can act on.