QuantZero profiles any neural network, scores its grid-aware carbon footprint, and compresses it with NeuroQuant mixed precision — so you measure the cost, then cut it, before you deploy.
Every inference burns power, and that power carries a carbon cost that depends on where it runs. QuantZero makes that cost visible — and then cuts it.
Profile the network layer by layer — FLOPs and parameters — then convert that into energy and grid-aware carbon for your deployment country.
NeuroQuant's cortical policy assigns INT8 to low-depth layers, BF16 to mid-depth, and keeps the rest FP32 — genuine mixed-precision tensors, not fake round-trips.
Receive the before/after carbon, energy savings, prediction-consistency proxy, and a downloadable EcoInfer certificate for your records.
The NeuroQuant cortical policy was validated in peer-reviewed research on ResNet-50 / ImageNet-1K. Both modes sit on the energy/accuracy Pareto frontier — you choose how aggressive to be.
Grid intensity ranges from 28 gCO₂/kWh in Norway to 713 in India — the same model can cost 25× more carbon depending on where it runs. QuantZero scores that, then shows the km-not-driven and tree-years saved per thousand inferences.
Calibrated for modest hardware — Raspberry Pi 4, Jetson Nano, laptops — where smaller, cooler models mean longer battery, lower cost, and a deployment that actually runs.
Computer labs and student projects running models on shared, older hardware.
Field deployments on laptops and edge boxes where every watt and dollar counts.
On-prem diagnostic models on modest servers and single-board computers.
Scan a library model or upload your own — see the footprint, then cut it.
Open the console