Training economics effect
Training economics
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.
How IOTA works
IOTA
Disaggregated machines, one unified system.
IOTA coordinates compute that is scattered, mixed, and always changing, unifying it as one system you can use.
The principle
Training shouldn’t need a perfect cluster.
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.
WORKLOADRESULT
Disaggregated training
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
One capability. Three payoffs.
The same disaggregated training works on all three constraints. Here is what it changes for each.
One common capability
Everything below comes from the single mechanism above, pointed at a different constraint.
Capacity effect
Capacity constraints
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
Infrastructure economics
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
One mechanism,
seen your way.
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.
WORKLOADRESULT
Active platform function
Workload setup
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.
Active platform function
Distributed execution
The run spreads across compute you do not own or reserve, and iota coordinates it as one system. Cheaper, more available hardware does the work a premium cluster used to, so the cost of the run comes down.
Active platform function
Progress continuity
Machines join and leave without stopping the run. Progress is saved as it goes, so a run finishes without the cost of restarting, and without paying for idle time waiting on stragglers.
Active platform function
Completed result
You get a trained model at a lower total cost than a reserved cluster, with a clear account of what it took to get there. More models become affordable, and you iterate more often for the same spend.
Active platform function
Available capacity
The compute you need is out there, but it is scattered, uneven, and often the wrong shape for conventional training. iota starts from that capacity, the capacity you could not use before.
Active platform function
Aggregation
iota draws that scattered capacity together into a single pool. Different generations, locations, and sizes of hardware become one usable whole, without you sourcing or stitching them yourself.
Active platform function
Coordination
iota coordinates the pooled capacity as one system, deciding what runs where and keeping the parts working together. Uneven hardware behaves as a single, dependable resource.
Active platform function
Continuous execution
The work runs on that capacity now, not whenever a cluster frees up. Machines can come and go and the run continues, so you start what you were waiting on instead of joining a queue.
Active platform function
Existing fleet
You already run the hardware. The problem is how much of it sits idle, or in shapes that conventional workloads will not touch. iota starts from the fleet you own.
Active platform function
Workload allocation
iota places training work onto the parts of the fleet that would otherwise sit unused, including capacity that is uneven or comes and goes. Work goes where there is room for it.
Active platform function
Productive utilisation
More of the fleet stays busy doing useful work, so utilisation rises without buying more hardware. The capacity you already paid for earns its keep.
Active platform function
More workloads served
With more usable capacity in play, workloads you could not serve profitably before become ones you can take on. The same fleet supports more demand.
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
The hard parts are ours, not yours.
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
Workload preparation
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.
Underlying capability
Coordination and orchestration
We decide what runs where, and keep thousands of moving parts agreeing on the state of the work. As machines join and leave, the plan updates around them, so the run always knows who is doing what.
Underlying capability
Distributed execution management
We move work and data between machines fast enough to keep everything fed, across ordinary networks rather than one internal fabric. Slow links and uneven bandwidth are handled underneath, so no machine sits waiting on another.
Underlying capability
Monitoring and failure detection
We watch the work continuously and catch problems early, including ones a provider has not spotted itself. A machine that slows, stalls, or starts to drift is flagged before it can hold the run back.
Underlying capability
Recovery and progression
When a machine drops out, its work is reissued and the system carries on, with progress preserved rather than lost. Capacity that disappears in one round is replaced in the next, so a failure costs a moment, not the run.
Underlying capability
Technical and economic reporting
We give a clear account of what the work achieved, how it converged, and what it cost, so the result is one you can act on. You see how the run performed and what it took to get there, not just the final model.
Where that matters
One capability, put to work three ways.
The mechanism is the same for everyone. What it unlocks depends on who you are and what you are trying to do.
Model developers and training teams
Train the models your budget and cluster access would rule out, and iterate more often for the same spend.
Run training on IOTA →AI platforms and model companies
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 →Advanced research and ML teams
Run demanding, experimental training on flexible capacity, without a reserved cluster sitting between you and the work.
Use IOTA →Next step
Two ways in. One platform underneath.
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.
Get Started →Train with IOTA
The managed route. Bring a defined workload, and we take it from qualification through to a result you can act on.
Talk to our Founding Team →