Zero Server Cooling
Because processing happens on-device, Vivral eliminates the massive water and electricity demands required to cool server farms.
Environmental Impact
Next-level intelligence. Fraction of the footprint.
Traditional AI relies on massive, energy-intensive cloud data centers. Vivral is fundamentally different. By running routine inference on-device on iPhone GPUs, we avoid remote GPU clusters, network hops, and server-farm cooling for everyday questions — cutting energy use by about 96% per query under our model.
Local Inference Architecture
About 96% less energy per query.
A frontier-class cloud query (~3–4T-parameter models) does not only burn GPU time — it pays for multi-GPU serving, data-center cooling (PUE), and network delivery. Our model puts a typical multi-thousand-token exchange at about 4.0 Wh (0.004 kWh) on that path.
Vivral answers on-device on the iPhone GPU / Neural Engine path. The same-size exchange is modeled at about 0.14 Wh (0.00014 kWh) — no remote GPU rack and no long-haul prompt round trip for routine help.
Energy per query
Cloud load vs on-device path
Per exchange of ~3,000 tokens. Bars are proportional to Wh.
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less energy / query
Architecture shift
Energy intensity: cloud frontier → on-device Vivral
Same ~3k-token exchange. Axis in Wh: 4.0 → 0.14 (~28× lower).
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Cloud frontier per query
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Vivral on-device per query
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Lower energy on the local path
Optimized for Mobile
Our proprietary medical models are quantized and distilled to run flawlessly on standard hardware without draining your daily battery life.
No Network Waste
Transmitting data over cellular and Wi-Fi networks is a hidden energy cost. Vivral's local approach keeps data—and energy use—grounded.