The Execution Fabric
for Modern Compute
Workloads have changed shape. Execution models haven't. TAHO Labs is on a mission to fit the work to the machines so the compute you already own finally does everything you paid for. Any hardware, any orchestrator, anywhere.
The problem
The execution gap
Peak compute per chip has risen roughly twenty times in eight years, but realized compute barely moved. Today's GPUs typically run at about 5% utilization across production clusters*.
The execution gap is the expanding pool of capacity that was paid for, powered, cooled, and never converted into useful work. Every bigger chip widens it. We founded TAHO Labs to solve it.
Chips progress, but capacity waste continues.
Sources: NVIDIA datasheets (Volta–Blackwell Ultra, FP16 dense); Rubin projected. Best-tuned MFU from Meta Llama 3, 2024. Typical from Cast AI 2026 (~23,000 production clusters).
The solution
Change the unit of execution.
Today, you reserve a whole machine for a workload that doesn't fit, so part of your processor struggles while most of it sits idle. TAHO splits the workload into small jobs and routes each to the resource that fits, so your hardware runs full instead of empty.
Traditional
18% used
Place the whole workload on a machine. Most of it sits idle.
TAHO
90% used
Decompose into small jobs (units). Route each to the resource that fits. Execute densely.
The payoff
Get what you paid for.
Both come from one move: packing the hardware you already pay for instead of leaving it idle.
Where TAHO lives
Below orchestration. Above silicon.
Jensen Huang says that AI is a five-layer cake: energy, chips, infrastructure, models, and applications. In this model, TAHO lives at the base of the infrastructure layer, directly above the silicon and underneath everything else.
TAHO's execution fabric removes compute bottlenecks. Everything gets faster and more affordable.
TAHO
Execution fabric, just above the silicon.
Compatibility
Nothing to rip out.
Nothing to rewrite.
Works with what you run
TAHO sits beneath your orchestration and serving layers. Everything above it keeps working, only faster.
- Kubernetes
- SLURM
- NVIDIA
- AMD
- CUDA
- ROCm
Runs where you run
Deploy on the infrastructure you already have: one environment, or across multiple clouds.
- Cloud
- On-prem
- Edge
Prove it in your stack
Bring a real workload. Experience the difference.
- Microsoft
- Google CloudComing soon