Rescue Your Stranded Capacity.
Most infrastructure forces an oversized workload onto a single processor, wasting most of its capacity. TAHO divides the work so it fits every processor you already own.
The mechanism
Not One Big Job, Thousands of Small Ones.
Modern AI workloads are too complex for a single processor, stranding much of its capacity. TAHO's Magnetic PeerMeshâ„¢ decomposes them into small jobs that fill the processor, using all of its capacity.
A job arrives whole, lands in TAHO and divides there into blocks, and the blocks fill whatever slots a sixteen-slot GPU has free. They run at the same time, so a nine-block job finishes in the time a one-block job takes. The finished job goes back together on the far side and ships out.
Watch What Changes
Same machines. Get more done.
TAHO runs beneath your orchestrator's scheduler, on the hardware you already have.
Traditional
Your scheduler gives each machine one whole job. The job holds the machine but runs a piece at a time, so most of the fleet is reserved and empty while the queue keeps growing.
With TAHO
TAHO decomposes your jobs into small jobs that fit the gaps. Every device in your fleet fills to capacity to get more done.
Two animations of the same jobs on the same machines run side by side. Under Traditional the scheduler places whole jobs, most of the fleet sits reserved and empty, and finished work returns slowly. With TAHO the same jobs are decomposed into blocks that fill the idle slots, and finished work returns several times faster over any 2-second window.
See it run
Three machines. One inference.
Real machines hand work back and forth in the terminal until it's done.
3 Machines Run One AI Inference as a Single Mesh
Getting started
Your workloads. Your infrastructure. Your timeline.
TAHO meets your stack where it is. Nothing above it has to change.
No migration
Your stack stays as-is. TAHO simply slots in underneath it.
No downtime
TAHO runs in parallel with what you have today. Nothing pauses to make room.
No minimum
One workload or ten thousand. Start where it makes sense and scale on your schedule.